Diagnostic uncertainty significantly impacts patient safety in emergency medicine, leading to missed diagnoses and severe harm. Current predictive models primarily emphasize diagnostic likelihood without explicitly addressing potential clinical harm from errors. We propose Triage and Risk Assessment via Cost Estimation (TRACE), a machine-learning framework that incorporates expected-value calculations, defined as the probability-weighted estimate of clinical harm, and patient similarity metrics to address both diagnostic accuracy and risk assessment. Using the Medical Information Mart for Intensive Care IV - Emergency Department dataset, we developed TRACE, comprising two modules: the expected value-powered triage index (TRACE-T), which calculates expected patient acuity from vital signs and chief complaints, and the patient similarity diagnosis engine (TRACE-Dx), which predicts diagnoses by identifying historically similar patients and weighing their outcomes by clinical harm. We assessed TRACE-T's predictive performance, our primary outcome, using decision trees, random forests, and Lasso (least absolute shrinkage and selection operator) regression. The TRACE-Dx predictions, our secondary outcome, were evaluated through string matching (comparing diagnostic text) and sentence embedding similarity (comparing diagnostic phrases). Our final analysis included a total of 2,501 patients from the dataset, due to requirements for diagnosis-string cleaning and computational demands of similarity calculations. Within this subset, TRACE-T significantly improved triage prediction accuracy, with the random forest classifier's accuracy increasing from 0.605 to 0.705 (P = .04) and demonstrating a notable reduction in root mean square error from 0.635 to 0.541 (P < .001). The decision tree model improved from 0.467 to 0.593 (P = .78) but did not reach statistical significance. The TRACE-Dx generated five expected value-ranked predicted diagnoses per encounter (12,505 predictions across 2,501 patients) and achieved average sentence embedding and string match similarities of 93.3% (95% CI, 92.7-94.0%) and 92.5% (95% CI, 90.7-94.3%), respectively, indicating strong alignment with actual outcomes. Expected value-based clinical harm modeling with patient similarity scoring enhances triage accuracy and diagnostic prediction in emergency care. Triage and Risk Assessment via Cost Estimation provides interpretable, actionable insights that could be incorporated into real-time clinical workflows as decision-support tools to reduce diagnostic uncertainty and improve patient outcomes.
Uncertainty affects at least 20% of primary care consultations, possibly leading primary care physicians to order additional investigations or referrals, affecting cost-effectiveness and patient safety. Experience is a key determinant in making these orders, along with anxiety or physicians' reactions to uncertainty. Past studies addressing the links between experience and additional investigations and referrals in uncertain situations have used questionnaires, database analyses, or interviews with general practitioners, but no study has used fully standardized conditions. This study aimed to examine the association between experience and orders for additional investigations and referrals in uncertain situations using a standardized virtual patient simulation. A cross-sectional study was conducted with 40 physicians stratified by sex and experience (<10 vs ≥10 years). Participants engaged in a simulated clinical scenario involving a man aged 69 years with atypical dyspnea designed to evoke diagnostic uncertainty. The virtual patient was presented via first-person video to assess the physicians' decision-making process. Participants' years of clinical experience, sex, age, place of practice, type of practice, number of in-office patients, duration of consultations, State-Trait Anxiety Inventory Form Y (STAI-Y) and Physician Reaction to Uncertainty scores, and diagnostic hypotheses were collected and analyzed using multivariate regression models. The group with <10 years of experience had higher STAI-Y (mean 41.3, SD 6.8 vs mean 32.7, SD 8.2; P<.001) and Physician Reaction to Uncertainty (mean 20.7, SD 5.4 vs mean 14.4, SD 6.9; P=.002) scores. Participants with <10 years of professional experience ordered more additional investigations and referrals on average: 10.2 (SD 3.4; 95% CI 8.9-11.7) vs 8.1 (SD 3.7; 95% CI 6.5-9.9; P=.03). There was no effect on costs: €153.80 (US $173.45) vs €129.60 (US $146.16) (effect size 0.296, 95% CI -0.348 to 0.939; P=.23). Multivariate analysis showed an association between the number of additional investigations and referrals with age (relative risk 0.980, 95% CI 0.963-0.997; adjusted P=.02) and mean STAI-Y score (relative risk 0.984, 95% CI 0.968-0.999; adjusted P=.04) but not with experience (<10 vs ≥10 years; R2=0.308). Less experienced physicians do not appear to overly rely on additional investigations and referrals in uncertain situations, suggesting that clinical reasoning remains a well-preserved skill among the younger generation of physicians. Future research could explore interventions targeting physicians' anxiety and tolerance to uncertainty as potential factors influencing requests for additional investigations and referrals. In practical terms, two avenues could be explored: specific training for supervisors to help them address uncertainty tolerance in the feedback they provide to trainees and the use of virtual consultations as a complement to traditional training, with particular attention to this aspect.
If a reference standard is affected by uncertainty in some patients, it can be considered to assign a probability for the presence of the target condition in these patients. Such probabilistic reference standards have been suggested or can be imagined in various settings. This paper aims at identifying conditions for unbiased estimation of sensitivity and specificity when a probabilistic reference standard is used in a diagnostic accuracy study. The conditional distribution of the index test given the probabilistic reference standard carries the information on sensitivity and specificity. An explicit expression for this distribution is derived. It includes three different model components reflecting a potential association of sensitivity with the probabilistic reference standard, a potential association of specificity with the probabilistic reference standard, and a potential mean miscalibration of the probabilistic reference standard. Simple parametrizations of the three different model components are suggested. The dependence of the conditional distribution on the parameter values is investigated, and the bias of model-based and model-free estimates is investigated in a simulation study. Due to identifiability issues, it can only be expected to be able to include one of the three components in a model-based estimation. If absence of the other two components can be assumed, model-based estimation allows estimates with negligible bias. Model-free estimates show a high risk of bias. The use of probabilistic reference standards in diagnostic accuracy studies is challenging. Unbiased estimation of sensitivity and specificity can only be expected if two out of the following three conditions can be ruled out: 1) Association of sensitivity with the probabilistic reference standard. 2) Association of specificity with the probabilistic reference standard. 3) Insufficient mean calibration of the probabilistic reference standard. If this is the case, adequate statistical methods allow estimation with negligible bias. Arguing for absence of two conditions requires corresponding reasoning. Evaluating the accuracy of a diagnostic test requires a systematic comparison of the test results with the true status in a series of patients. Determining the true status by a so-called reference test can be challenging in some patients. It has been suggested that the reference test should then result in a probability instead of a definite decision about the true status. It is unknown how to make use of such a probabilistic reference standard in analysing the diagnostic accuracy of the index test - i.e. the test of interest. This paper investigates how to estimate two key accuracy parameters - sensitivity and specificity - based on a probabilistic reference standard. Three conditions which challenge an unbiased estimation are identified: 1) Patients with a true positive status but difficult to diagnose for the index test are assigned lower probabilities than those easy to diagnose. 2) Patients with a true negative status but difficult to diagnose for the index test are assigned higher probabilities than those easy to diagnose. 3) The probabilistic reference standard systematically over- or underestimates the true status of the patients. If two of the three conditions can be excluded, it is possible to obtain unbiased estimates by adequate statistical methods. It is recommended to carefully discuss the potential presence of the three conditions whenever a probabilistic reference standard is used.
Dementia, cardiovascular disease (CVD), and functional impairment (FI) often co-occur in aging populations, with abdominal obesity as a shared modifiable risk factor. The long-term impact of abdominal obesity on these comorbidities is unclear. We projected the 30-year burden of dementia, FI, and CVD in China under different trajectories of abdominal obesity prevalence. We modeled three trajectories of abdominal obesity prevalence from 2020 to 2050 using data from the China Health and Nutrition Survey (2000-2015): continuation of the observed growth prevalence trend (persistent), stabilization at 2015 levels (optimal), and a 50% reduction in the growth rate (improved). Abdominal obesity was defined as a waist circumference of ≥90 cm for men and ≥85 cm for women. A Markov model was used to estimate occurrence of dementia, CVD, FI, and mortality among adults aged ≥65 years by sex and year. Under the persistent scenario, dementia cases were projected to rise to 37.8 million (95% uncertainty interval (UI) [36.7, 38.9]) by 2050, alongside 68.1 million (95% UI [66.7, 69.5]) cases of FI, 198.3 million (95% UI [196.5, 200.4]) CVD cases, and 17.6 million (95% UI [16.5, 18.7]) deaths. Compared with the persistent scenario, dementia and FI burdens increased under the optimal and improved scenarios by 2050, by 1396.0 thousand (95% UI [589.1, 2293.9]) and 711.4 thousand (95% UI [294.4, 1150.3]) for dementia, and by 2570.6 thousand (95% UI [1081.0, 4024.9]) and 1289.5 thousand (95% UI [569.5, 2034.2]) for FI, mainly due to reduced CVD mortality expanding the population at risk. These shifts are most pronounced among adults aged ≥80 years and women. For CVD, reductions in the number of cases were projected in the short term (by 2030), but these changes remain uncertain by 2050. Main limitations include the assumption that other risk factors remain unchanged, and the lack of modeling of multiple co-occurring dementia's risk factors. Abdominal obesity control may reduce CVD incidence and mortality, thereby shifting the disease burden toward dementia and FI due to increased longevity, highlighting the need for integrated, life-course public health strategies responsive to the patterns of dementia and its comorbidity in older people.
Individuals with cirrhosis frequently require Emergency Department (ED) care, with some experiencing repeated ED use, yet little is known about the patient and caregiver perspectives driving decisions to visit the ED. We aimed to explore perspectives of including high ED utilizers with cirrhosis and their caregivers to identify drivers of ED use and opportunities to optimize care. Using human-centered design methods, we conducted an in-person group engagement session with 7 adults with cirrhosis and their caregivers, recruited from recent ED encounters. A custom board-game activity facilitated the discussion. Data were analyzed using snippet extraction, affinity mapping, and affinity concept modeling. Seven major themes emerged: (1) Mindset around symptoms, which includes fear, uncertainty, and caregiver burden. (2) Informational needs, including reliance on variable-quality online resources and lack of trusted education. (3) Day-to-day cirrhosis management, particularly challenges related to medications and symptom monitoring. (4) Symptom-driven ED triggers, with some prompting, urgent visits. (5) Decision-making factors, including limited alternatives to ED care and prior experiences, and mismatched patient-caregiver thresholds for seeking care. (6) Expectations of ED care, focused on pain relief and return to baseline health. (7) Challenges during ED care, including long wait times, misdiagnosis concerns, and stigma related to pain treatment. Concept modeling revealed that ED decision-making is a dynamic journey shaped by symptom severity, emotional states, logistical considerations, and evolving patient-caregiver-provider roles. Pain, uncertainty about symptom severity, and lack of accessible real-time clinical support were major drivers of ED utilization in cirrhosis. Interventions addressing these specific needs may reduce avoidable ED use. These findings provide a patient-informed foundation for care delivery redesign in cirrhosis.
High fasting plasma glucose (HFPG) is the second dominant metabolic risk factor contributing to the global burden of pancreatic cancer (PC). However, detailed investigations into the spatiotemporal patterns of PC burden attributable to HFPG remain limited. This study aims to assess global, regional, and national trends in PC mortality and disability-adjusted life years (DALYs) attributable to HFPG from 1990 to 2021. This longitudinal observational study was based on data from the global burden of disease 2021 study, covering data from 204 countries and territories. We extracted mortality, DALYs, age-standardized mortality rate (ASMR), and age-standardized DALY rate (ASDR) for PC attributable to HFPG. These metrics were stratified by sex, age group, country, and socio-demographic index (SDI). Temporal trends were evaluated using the estimated annual percentage change (EAPC) for ASMR and ASDR between 1990 and 2021. In 2021, an estimated 132,753 (95% uncertainty interval [UI]: 15,077-252,345) deaths and 2,751,644 (95% UI: 315,351-5,201,444) DALYs were attributable to HFPG, accounting for 40.9% and 39.3% of total PC-related deaths and DALYs, respectively. From 1990 to 2021, the number of HFPG-attributable PC deaths and DALYs increased by 234.1% and 209.7%, respectively. Substantial regional and national disparities were observed in the burden of PC attributable to HFPG. The highest ASMR and ASDR were recorded in high-SDI regions. Among global regions, East Asia reported the largest number of HFPG-attributable PC deaths and DALYs. The burden was also disproportionately higher among males and older adults. Notably, both ASMR and ASDR were significantly inversely correlated with EAPC. The global burden of PC attributable to HFPG has risen substantially over the past 3 decades, with marked regional and demographic disparities. These findings underscore the urgent need for glycemic control strategies and nutrition-based public health interventions to reduce HFPG-related cancer burden, particularly among high-risk populations.
Meningitis remains the leading infectious cause of neurological disabilities globally, disproportionately affecting children younger than 5 years and populations in the African meningitis belt. Whereas previous global estimates focused on ten pathogen categories, this study presents the most comprehensive analysis to date, assessing the meningitis burden attributable to 17 causative pathogens based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 framework. GBD is a systematic, scientific effort aimed at quantifying the comparative magnitude of health loss caused by diseases, injuries, and risk factors across age groups, sexes, and geographical locations over time. We estimated meningitis mortality using the Cause of Death Ensemble model (CODEm) and morbidity using DisMod-MR 2.1, incorporating data from vital registration, verbal autopsy, surveillance, hospital data, and systematic reviews. Aetiology-specific estimates were generated with pathogen-linked case-fatality ratios and splined binomial regression models. Risk factor attribution was based on established risk-outcome pairs and population attributable fractions. In 2023, there were 259 000 (95% uncertainty interval 202 000-335 000) global deaths and 2·54 million (2·20-2·93) incident cases of meningitis. Children younger than 5 years accounted for more than a third of deaths (86 600 [53 300-149 000]). Streptococcus pneumoniae, Neisseria meningitidis, non-polio enteroviruses, and other viruses were the leading causes of death, while non-polio enteroviruses caused the most cases. The four WHO-defined preventable meningitis pathogens of interest (S pneumoniae, N meningitidis, Haemophilus influenzae, and Group B streptococcus) contributed to 98 700 deaths (77 000-127 000) and 594 000 cases (514 000-686 000). Low birthweight, short gestation, and household air pollution were the top risk factors for meningitis-related mortality. Although mortality and incidence have declined significantly since 1990, progress is insufficient to meet WHO 2030 targets. Despite marked progress in reducing bacterial meningitis via global vaccination campaigns, a substantial meningitis burden persists, attributable both to common pathogens such as S pneumoniae and N meningitidis and to emerging non-bacterial pathogens such as Candida spp and drug-resistant fungi. Achieving WHO goals will require sustained investment in surveillance, vaccination, maternal screening, and health-system strengthening, especially in high-burden settings. Gates Foundation, Wellcome Trust, and UK Department of Health and Social Care.
Information on childhood cancer burden is crucial for effective cancer policy planning. Unfortunately, observed paediatric cancer data are not available in every country, and previous global burden estimates have not discretely reported several common cancers of childhood. We aimed to inform efforts to address childhood cancer burden globally by analysing results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, which now include nine additional cancer causes compared with previous GBD analyses. GBD 2023 data sources for cancer estimation included population-based cancer registries, vital registration systems, and verbal autopsies. For childhood cancers (defined as those occurring at ages 0-19 years), mortality was estimated using cancer-specific ensemble models and incidence was estimated using mortality estimates and modelled mortality-to-incidence ratios (MIRs). Years of life lost (YLLs) were estimated by multiplying age-specific cancer deaths by the standard life expectancy at the age of death. Prevalence was estimated using survival estimates modelled from MIRs and multiplied by sequelae-specific disability weights to estimate years lived with disability (YLDs). Disability-adjusted life-years (DALYs) were estimated as the sum of YLLs and YLDs. Estimates are presented globally and by geographical and resource groupings, and all estimates are presented with 95% uncertainty intervals (UIs). Globally, in 2023, there were an estimated 377 000 incident childhood cancer cases (95% UI 288 000-489 000), 144 000 deaths (131 000-162 000), and 11·7 million (10·7-13·2) DALYs due to childhood cancer. Deaths due to childhood cancer decreased by 27·0% (15·5-36·1) globally, from 197 000 (173 000-218 000) in 1990, but increased in the WHO African region by 55·6% (25·5-92·4), from 31 500 (24 900-38 500) to 49 000 (42 600-58 200) between 1990 and 2023. In 2023, age-standardised YLLs due to childhood cancer were inversely correlated with country-level Socio-demographic Index. Childhood cancer was the eighth-leading cause of childhood deaths and the ninth-leading cause of DALYs among all cancers in 2023. The percentage of DALYs due to uncategorised childhood cancers was reduced from 26·5% (26·5-26·5) in GBD 2017 to 10·5% (8·1-13·1) with the addition of the nine new cancer causes. Target cancers for the WHO Global Initiative for Childhood Cancer (GICC) comprised 47·3% (42·2-52·0) of global childhood cancer deaths in 2023. Global childhood cancer burden remains a substantial contributor to global childhood disease and cancer burden and is disproportionately weighted towards resource-limited settings. The estimation of additional cancer types relevant in childhood provides a step towards alignment with WHO GICC targets. Efforts to decrease global childhood cancer burden should focus on addressing the inequities in burden worldwide and support comprehensive improvements along the childhood cancer diagnosis and care continuum. St Jude Children's Research Hospital, Gates Foundation, and St Baldrick's Foundation.
Under high renewable energy penetration, wind power forecasting faces pronounced challenges due to strong randomness and uncertainty, making conventional point-forecast-centric paradigms insufficient for risk-aware and reliable power system scheduling. An uncertainty-aware temporal transformer framework for wind power forecasting is presented, integrating probabilistic modeling with deep temporal representation learning to jointly optimize prediction accuracy and uncertainty characterization. Crucially, rather than treating uncertainty quantification merely as a post-processing step, the central conceptual contribution lies in modularizing uncertainty directly within the attention mechanism. A probability-driven temporal attention mechanism is incorporated at the encoding stage to emphasize high-variability and high-risk time slices during feature aggregation, while a multi-quantile output and interval modeling strategy is adopted at the prediction stage to directly learn the conditional distribution of wind power, enabling simultaneous point and interval forecasts with statistical confidence. Extensive experiments on multiple public wind power datasets demonstrate that the proposed method consistently outperforms traditional statistical models, deep temporal models, and deterministic transformers, as validated by formal statistical significance testing. Specifically, the method achieves an MAE of 0.089, an RMSE of 0.132, and a MAPE of 10.84% on the test set, corresponding to reductions of approximately 8%-10% relative to the deterministic transformer. In uncertainty evaluation, a PICP of 0.91 is attained while compressing the MPIW to 0.221 and reducing the CWC to 0.241, indicating a favorable balance between coverage reliability and interval compactness. Compared with mainstream probabilistic forecasting methods, the model further reduces RMSE while maintaining coverage levels close to the 90% target, effectively mitigating excessive interval conservatism. Moreover, by adaptively generating heteroscedastic intervals that widen during high-volatility events and narrow under stable conditions, the model achieves a highly focused and effective capture of critical uncertainty information.
Background University students experience high levels of academic and psychological stress, yet counseling services remain underutilized, particularly in Middle Eastern settings, where stigma and limited awareness may affect help-seeking. Although studies in the United Arab Emirates (UAE) have examined general attitudes toward professional psychological help-seeking, institution-specific evidence on students' awareness of campus counseling services, perceived confidentiality, and actual utilization remains limited. In particular, data describing these factors among students at the University of Sharjah (UOS) are lacking. This study assessed awareness, attitudes, and practices related to counseling services among students at UOS. Methods A descriptive cross-sectional survey was conducted among undergraduate students at UOS between March and June 2023 using a bilingual, self-administered questionnaire. Convenience sampling yielded 491 participants. Data were analyzed using descriptive statistics and Chi-square tests (p ≤ 0.05). As a non-probability convenience sample, the findings may be subject to selection bias and limited generalizability. Results Most participants were aged 18-24 years (462, 94.1%) and female (354, 72.1%). Overall, 307 (62.5%) were unaware of university counseling services, and 322 (65.6%) reported confidentiality concerns. High academic stress was common, with 389 (79.2%) reporting difficulty managing stress and time during examinations. Despite this, only 34 (8.7%) of stressed students used university counseling services. Friends (187, 38.0%) and family (167, 34.0%) were the main support sources. Major barriers included lack of awareness of service location (218, 44.4%), reluctance to share personal information (199, 40.5%), and uncertainty about access (177, 36.1%). Female gender was associated with greater stress and study difficulty (302, 85.3% vs 87, 63.5%; χ² = 5.64, p = 0.018, Cramér's V = 0.11), with females showing higher odds of stress than males (OR = 3.33, 95% CI: 2.08-5.33). Face-to-face (398, 81.1%) and individual counseling (445, 90.6%) were preferred. Conclusions A substantial gap exists between students' mental health needs and counseling utilization at UOS. Limited awareness, stigma-related concerns, and accessibility barriers contribute to underuse. Enhancing outreach and improving the visibility of and trust in services may increase counseling engagement among UAE university students.
The PReDicT study showed that predictive algorithm-guided antidepressant treatment reduces anxiety and improves functioning in patients with depression. To estimate the costs, outcomes and cost-effectiveness of the PReDicT test compared with treatment as usual (TAU) for primary depression care in five European countries. Within-trial economic analysis was conducted over 24 weeks from the health/social care and societal perspectives alongside the PReDicT trial (NCT02790970) in France, Germany, The Netherlands, Spain, and the UK, according to Consolidated Health Economic Evaluation Reporting Standards guidelines. We calculated quality-adjusted life-years (QALYs) based on the EQ-5D-5L, capability-weighted life-years based on the Oxford Capabilities Questionnaire - Mental Health (OxCAP-MH) (Germany and UK only), and costs for 2018 (€). Multiple imputation for missing data, multivariable regression for cost and outcome differences, and bootstrapping and sensitivity analyses for uncertainty were conducted. There were significant outcome improvements (EQ-5D-5L PRedicT: +0.139; TAU: +0.140) and societal cost reductions (PRedicT: -€2589; TAU: -€2602) in both groups (N = 913) between the before and during trial periods. In the UK and Germany (n = 619), the PReDicT group showed significant additional capability well-being gains (OxCAP-MH: +2.127, p = 0.021). Cost-effectiveness probabilities ranged from 46 to 59% at trial level, but exceeded 80% in the UK. Results remained stable across different sensitivity analyses, with societal cost-effectiveness improved for those (self-)employed. We observed potentially meaningful health and economic benefits of closely monitored antidepressant treatment, as implemented in both treatment and control arms of the PReDicT trial. The PReDicT test itself had some added benefits in improved capabilities and productivity, however, with great uncertainty and country-level variations in cost-effectiveness.
The increasing prevalence of most non-communicable diseases in Italy represents a major public health challenge, largely influenced by modifiable risk factors. This study aims to analyse time trends and subnational differences in the burden of disease attributable to risk factors in Italy, from 1990 to 2023. We used estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study 2023 to assess the disease burden attributable to risk factors across five Italian macro regions between 1990 and 2023. Burden was measured using disability-adjusted life years (DALYs), reported as all-age and age-standardised rates per 100 000 population. Correlations between the Socio-demographic Index (SDI) and DALYs attributable to behavioural, metabolic, and environmental or occupational risk factors were assessed. Between 1990 and 2023, metabolic risks in males declined nationally (-7·3% [95% uncertainty interval (UI) -14·1 to -0·2]) but rose in the South and remained stable in the Islands. In females, they were stable overall (-0·4% [-10·3 to 8·4]) but increased in the South and Islands. Behavioural risks decreased across all macro regions in both sexes. DALYs from metabolic risks were strongly and inversely correlated with SDI in both sexes (r=-0·79, p<0·001), whereas behavioural risks correlated negatively with SDI only in males (r=-0·66, p=0·001). During the same time, the proportion of unattributable DALYs increased from 48% to 58% in males and from 60% to 65% in females. Despite overall improvements in attributable burden between 1990 and 2023, substantial geographical disparities and sex differences persist, underscoring the need for stronger tobacco control, gender-sensitive interventions on metabolic risks, and the integration of social determinants into health policy. Gates Foundation and Italian Ministry of Health (Ricerca Corrente) to Institute for Maternal and Child Health - IRCCS Burlo Garofolo.
Machine learning and artificial intelligence are increasingly applied to medical diagnostics and clinical decision-making. To evaluate model performance, the F 1 $$ {F}_1 $$ score and its generalized form, the F β $$ {F}_{\beta } $$ score, are widely used as they balance precision and sensitivity. However, rigorous statistical inference and power analysis for the F 1 $$ {F}_1 $$ and F β $$ {F}_{\beta } $$ scores remain limited. In this study, we propose psF1, a unified and comprehensive framework for interval estimation, hypothesis testing, and power and sample size calculation for both single and comparative F 1 $$ {F}_1 $$ and F β $$ {F}_{\beta } $$ scores. psF1 leverages exact probability distributions as well as approximations for large sample sizes to provide valid statistical inference and power analyses. Extensive simulations demonstrate the accuracy and robustness of psF1 across a range of sensitivity, precision, and sample size scenarios. We further showcase its practical utility through real-world biomedical classification tasks. This framework enables principled evaluation and comparison of classifiers using F 1 $$ {F}_1 $$ and F β $$ {F}_{\beta } $$ scores with reliable uncertainty quantification and informed sample size planning. psF1 is freely available at http://github.com/cyhsuTN/psF1.
Recent drinking water regulations have imposed remediation for per- and polyfluoroalkyl substances (PFAS). In response, treatment facilities may be required to retrofit existing treatment schemes to treat PFAS below maximum contaminant levels (MCLs). Adsorption technologies such as granular activated carbon (GAC) and ion exchange (IX) have been demonstrated to be effective; however, there are limited techno-economic metrics available that provide guidance on technology selection and design for diverse PFAS-containing source water conditions. Process systems engineering (PSE) tools that traditionally perform these analyses are hindered by the data availability, model validity, and understanding of treatment phenomena for emerging contaminants. This work employs published data regressions, statistical models, process models, techno-economic analyses, and other process systems tools in a model-based uncertainty framework to consider the limitations of emerging contaminant research. Through this analysis framework, economic results are provided as probabilistic distributions based on the uncertainty of the models and diverse conditions that treatment facilities experience. Regressed parameter distributions and model predictive performance trends for each technology are identified based on PFAS structure and chain length. GAC systems are evaluated at consistently lower levelized costs of water (LCOWs) with less economic risk over IX systems considering uncertainty across most design conditions and PFAS species. Both technologies are evaluated to have comparable adsorbent usage intensity on a volume basis, indicative of similar sustainability.
The increasing penetration of photovoltaic distributed generation (PV-DG) in Radial Distribution Systems (RDSs) plays a vital role in achieving sustainable energy transition objectives; however, the inherent uncertainty associated with solar irradiance and load demand poses significant challenges to optimal planning and operation. This paper presents a stochastic optimization framework for PV-DG allocation in RDSs using the Barrel Theory-Based Optimizer (BTO). Uncertainties in solar irradiance and load demand are explicitly modeled using appropriate probability density functions and efficiently represented through a higher-order Point Estimate Method (PEM), which captures the essential statistical characteristics with a limited number of representative scenarios. The proposed framework simultaneously optimizes the location and capacity of PV-DG units to minimize real power losses and enhance voltage profile performance while ensuring system operational constraints are satisfied. The effectiveness of the proposed approach is validated on the 85-bus and the IEEE 118-bus RDSs, where the BTO exhibits superior convergence characteristics and enhanced solution robustness when compared with several benchmark optimization techniques, including the well-established Differential Evolution Algorithm (DEA), the recent Crocodile Ambush Optimization (CAO, 2025), and the Schrödinger Optimizer Algorithm (SOA, 2025). For the 85-bus RDS, the impact of integrating different numbers of PV units is systematically investigated. Simulation results confirm that the proposed BTO-based stochastic planning strategy significantly improves energy efficiency, voltage regulation, and loss reduction, thereby enhancing the overall sustainability of the RDS. For the 85-node RDS, the BTO achieves a noticeable reduction in average real power losses, outperforming DEA, CAO, and SOA by 2.55%, 4.10%, and 6.74%, respectively, when three PV units are installed. Additionally, for the case of four PV units, the proposed BTO yields even greater improvements, with loss reductions of 5.12%, 7.50%, and 14.12%, respectively, compared with the same benchmark algorithms. Furthermore, for five PV units, the BTO achieves much greater reduction, outperforming DEA, CAO, and SOA by 13.05%, 6.45%, and 32.31%, respectively, when three PV units are installed.
Bounded random variables arise naturally in physical, engineering, and reliability systems when measurements represent proportions, efficiencies, normalized intensities, or constrained state variables. In this paper, a flexible bounded stochastic framework generated through a beta transformation of the Kumaraswamy (Kw) baseline is introduced, yielding a four-parameter family capable of capturing diverse boundary behaviors and hazard rate (HR) structures. Rigorous theoretical properties of the proposed model are developed, including structural identifiability, limiting behavior at the boundaries, shape characteristics of the probability density function (PDF) and HR functions, and explicit stochastic representations. Closed-form expressions for moments, probability-weighted moments are derived under mild regularity conditions, together with comprehensive information-theoretic characterizations based on Shannon, Rényi, and Tsallis entropies, as well as Kullback-Leibler divergence relative to baseline models. Likelihood-based inference is studied in detail, with explicit score functions, Fisher information, and asymptotic properties of the maximum likelihood estimators (MLE) established. An illustrative application to bounded measurements from an engineered system demonstrates the practical relevance of the theoretical results. The proposed framework provides a mathematically rigorous and interpretable tool for uncertainty quantification and reliability analysis of bounded physical quantities.
The link between long-term protein intake and muscle performance in older adults has been hard to define, partly because most studies rely on short dietary windows and are vulnerable to confounding and measurement noise. In this work, we attempted to estimate the usual protein intake and functional limitation among U.S. adults aged ≥ 60 years using a target-trial emulation framework with overlap weighting and semiparametric estimators. Data were drawn from four NHANES survey cycles (2011-2018), including 5,736 adults aged ≥ 60 years with complete exposure, outcome, and covariate data. Usual protein intake (g/kg/day) was derived from available 24-hour recalls to approximate habitual intake. The primary outcome was PFQ-defined mobility limitation across cycles; grip strength (2011-2014) was analyzed separately as a secondary outcome. Causal contrasts across predefined intake categories (<0.8, 0.8- < 1.0, 1.0- < 1.2, ≥1.2 g/kg/day) were evaluated using covariate-balancing propensity score overlap weighting (ATO estimand) followed by marginal structural models. Doubly robust sensitivity analyses were conducted using augmented inverse probability weighting and targeted maximum likelihood estimation with generalized linear models. Simulation extrapolation (SIMEX) was applied to assess potential bias from dietary measurement error. Exploratory analyses evaluated hs-CRP as a potential mediator and tested effect modification by vitamin D status and physical activity. Mean usual protein intake was 0.93 g/kg/day, and approximately 42% of participants consumed at least 0.8 g/kg/day, the current Recommended Dietary Allowance (RDA) for the general adult population. In the prespecified overlap-weighted marginal structural model (ATO estimand), higher intake was associated with lower odds of mobility limitation, although the primary contrast comparing ≥ 1.2 versus < 0.8 g/kg/day was modest and not statistically significant (OR 0.89, 95% CI 0.54-1.47). A doubly robust binary contrast yielded a -6.6 percentage-point difference in predicted limitation (95% CI -25.8 to 12.7), consistent in direction but imprecise. In cycle-specific analyses, the inverse association was more pronounced in 2015-2018 (OR 0.80, 95% CI 0.65-0.98). Spline models suggested a steeper decline in predicted limitation below approximately 1.0-1.1 g/kg/day, with a flatter trajectory at higher intakes. Exploratory mediation models indicated a potential indirect component through hs-CRP, though these estimates were not overlap-weighted and should be interpreted cautiously. Higher usual protein intake was directionally associated with lower odds of mobility limitation among older U.S. adults within a target trial emulation framework, although the primary overlap-weighted estimates were modest and imprecise. Evidence of nonlinearity suggests that intakes near 1.0-1.1 g/kg/day may mark a range where predicted limitation declines more steeply, but uncertainty increases at higher intake levels. Given the cross-sectional design and residual potential for confounding, these findings should be interpreted cautiously. Prospective studies are needed to determine whether sustained protein intake in this range meaningfully preserves functional capacity over time.
Severe injuries in recreational alpine skiing and snowboarding impose disproportionate clinical and societal burden. Evidence on modifiable countermeasures beyond helmets remains fragmented and may vary across risk profiles and exposure conditions. This study aimed to identify factors associated with severe injuries among recreational skiers and snowboarders, and to examine nonlinear dose-response relationships and effect modification by preventive practices. We conducted a retrospective cross-sectional injury-severity study of injured adult skiers and snowboarders treated at resort medical clinics and emergency departments at two ski resorts in Zhangjiakou, China, across three winter seasons from 2021 to 2024. Severe injury was defined as an injury severity score (ISS) above 15. We modeled severe injury conditional on injury using Firth-penalized logistic regression. Restricted cubic splines were applied for age, body mass index, temperature, and snow depth to assess nonlinear associations. Prespecified interaction blocks were tested using joint Wald tests with false discovery rate control, and scenario-standardized impact metrics were estimated with bootstrap uncertainty. Among 2,369 injured participants, 339 (14.3%) sustained severe injuries. In fully adjusted models, knee protector use (OR = 0.57, p = 0.005), cautious risk behavior (OR = 0.46, p < 0.001), and advanced skill level (OR = 0.46, p = 0.023) were associated with lower odds of severe injury, whereas collisions with other participants were associated with higher severity (OR = 1.51, p = 0.011). Dose-response analyses suggested non-linear patterns across continuous variables, with statistically significant overall evidence for snow depth. Warm-up and binding tests showed stronger protection in high-risk subgroups such as beginners and risk-takers, although scenario estimates were imprecise. Severe outcomes among injured snow-sport participants reflect interacting exposure intensity, environment, and modifiable practices. Findings support risk-stratified prevention and condition-aware resort safety operations.
Cancer diagnosed during pregnancy presents unique challenges, requiring women to navigate treatment alongside pregnancy and early parenthood. While clinical aspects are well studied, the psychosocial impact on survivorship remains underexplored. This study examined the lived experiences of women diagnosed during pregnancy, focusing on emotional, psychological and practical challenges from diagnosis through survivorship. A qualitative study was conducted using interview data from 20 women in the UK diagnosed with cancer during pregnancy. Participants were recruited via Mummy's Star, a charity supporting individuals affected by cancer in pregnancy. Interviews were thematically analysed using template analysis, focusing on decision-making, psychosocial burden and support needs. Six inter-related themes were identified: (1) managing cancer with uncertainty, women reported distress due to delayed investigations and treatment adjustments during pregnancy; (2) ethical decision-making, emotionally charged choices around treatment, birth and feeding were made with limited or conflicting information; (3) balancing cancer and its treatment with pregnancy and family life, early parenting was disrupted; (4) work disruption and financial strain, treatment-related costs and lost income caused hardship; (5) emotional impact of diagnosis and treatment, including lasting psychological effects; and (6) Coping and support, guilt, fear of recurrence and unmet support needs persisted post-treatment. Women diagnosed with cancer in pregnancy face profound, long-term emotional and financial challenges. Fragmented care and inadequate support exacerbate these difficulties. Integrated multidisciplinary care is essential to improving survivorship.
Accurate mosquito surveillance is essential for guiding targeted interventions. To capture the spatial and temporal heterogeneity of mosquito populations, surveillance designs would ideally be flexible and evidence-based, that is, informed by prior data. This study compares a newly developed entomological adaptive surveillance framework (EASF) and routine entomological surveillance in Ghana and Mozambique. Routine surveillance refers to standard practices by National Malaria Control Programmes, while EASF enables the strategic selection of sampling locations based on modelled risk or environmental criteria. The study evaluates both approaches based on Anopheles mosquito catch rates, model predictive performance and the proportion of obsolete sampling locations-those contributing little to overall surveillance outcomes and model improvement. A Bayesian framework for exceedance probability is employed in this adaptive design to select the number and location of the adaptive sites. Estimates are based on a Bayesian spatiotemporal model with nugget effects to analyse mosquito abundance, using log-transformed counts to address heavy-tailed distributions. The EASF outperformed routine surveillance in most metrics, including mosquito catch rates, model robustness and reduction in uncertainty. Notably, when standardised, EASF sites yielded higher mosquito catches and more stable predictions, as indicated by lower coefficients of variation. While EASF generally improved model inference and predictive accuracy, performance varied by country. Nevertheless, EASF consistently identified proportionally fewer obsolete locations than routine surveillance, demonstrating its efficiency in targeting informative sites. EASF offers an effective, evidence-based approach to improving surveillance precision, enabling surveillance programmes to dynamic transmission systems and emerging needs while maintaining operational feasibility. Integrating adaptive and routine designs can enhance surveillance efficiency, either by improving accuracy, reducing site numbers or accelerating detection of ecological changes. The key to effective entomological surveillance is not rigidly achieving a target, but continuously adapting towards it.