Electrifying the transportation sector is a key climate-change mitigation strategy. Reductions in exhaust emissions have anticipated air quality co-benefits; yet, evidence is primarily based on projections. Using observed data in California, USA, we aimed to investigate whether reductions in exhaust emissions from the transition to zero-emissions vehicles (ZEVs: battery electric, plug-in hybrid, and hydrogen fuel cell) were detectable using Tropospheric Monitoring Instrument (TROPOMI) satellite measurements of nitrogen dioxide (NO2) air pollution. In this longitudinal observational study, we combined data from 2019 to 2023 on annual light-duty ZEV registrations in 1692 California ZIP code tabulation areas (ZCTAs; cross-walked from ZIP codes) with annual mean TROPOMI-measured NO2. We used longitudinal linear mixed-effects models to assess the association between within-ZCTA ZEV changes and within-ZCTA NO2 changes, adjusting for temporal trends and time-varying potential confounding, or excluding 2020. In positive control analyses, we related internal combustion engine vehicle registrations to NO2. In ground-truth analyses, we related ZEVs to NO2 concentrations using 123 Environmental Protection Agency monitors from 2012 to 2023. The median within-ZCTA increase in ZEVs from 2019 to 2023 was 272 (IQR 18 to 839). A within-ZCTA increase of 200 ZEVs was associated with a 1·10% (95% CI -1·19 to -1·00) decrease in annual average NO2. The main findings were supported by sensitivity analyses (-1·32% [-1·43 to -1·21] when excluding the year 2020), ground-truth analysis (-0·87% [-1·76 to 0·03] using NO2 from ground-level monitors), and positive control analysis (0·80% [0·63 to 0·97] increase in annual average NO2 per 800 increase in number of internal combustion engine vehicles). Using a natural experiment, we found that within-ZCTA increases in ZEV registrations were associated with reductions in NO2 air pollution measured by satellite and replicated with ground-level monitors. This work in California serves as a proof-of-principle for future work using satellite-measured NO2 to quantify effects of climate-change mitigation efforts on combustion-related air pollution within the USA and internationally. National Institutes of Health, National Institute of Environmental Health Sciences, National Aeronautics and Space Administration Health and Air Quality Applied Sciences Team, and the National Aeronautics and Space Administration Atmospheric Composition Modeling and Analysis Program.
Historically, resection followed by chemotherapy was standard treatment for stage II-III non-small cell lung cancer (NSCLC) and selected patients with stage IB disease. However, recurrence was common post-resection. Genomic characterization of early-stage NSCLC and precision medicine have since provided more effective therapies. Real-world evidence prior to practice-changing approvals aids understanding of the disease and treatment landscape, and establishment of benchmarks for future studies. We report an international, retrospective, real-world study of patients with early-stage NSCLC. Medical records were reviewed for patients ≥18 years with completely resected stage IA-IIIA NSCLC (diagnosed January2014-December2017) and an EGFR mutation test result from centers in Austria, Canada, France, Germany, Republic of Korea, Taiwan, the UK, and the US. Data were analyzed from diagnosis until December 2020 (UK until February 2021). Primary objectives included the proportion of patients with EGFR mutation-positive NSCLC, treatment patterns, and overall survival (OS). Analyses were descriptive, with OS estimated using Kaplan-Meier methods. Of 1043 patients (stage IA: 35%; IB: 22%; IIA: 15%; IIB: 10%; IIIA: 18%), 330 (32%) had EGFR mutation-positive NSCLC. Fifty-two percent of patients underwent surgical resection only; 29% (predominantly stage II-IIIA) received surgery plus adjuvant treatment ≤26 weeks post-surgery. The most common adjuvant treatment was chemotherapy for EGFR mutation-positive (97/103; 94%) and -negative NSCLC (193/195; 99%). Five-year OS rates were 84% (median follow-up 60.8 months) and 64% (median follow-up 51.3 months) for patients with EGFR mutation-positive and -negative NSCLC, respectively. Lung and brain were the most common sites of recurrence. In this real-world study, prior to the approval of osimertinib as adjuvant treatment for resected NSCLC, only one-third of patients received adjuvant treatment within 26 weeks post-surgery, highlighting its underutilization and emphasizing the critical need for early EGFR mutation testing to inform optimal treatment choices.
Risk stratification strategies in primary prevention of coronary events lack precision. To determine whether prediction of first coronary events is improved by adding information on coronary atherosclerosis from coronary computed tomography angiography (CCTA) to a model using the pooled cohort equation (PCE) risk score tool and the coronary artery calcification score (CACS). Observational cohort study including individuals aged 50 to 64 years randomly recruited from the general population and examined at 6 university hospitals in Sweden from 2013 to 2018, with a median follow-up of 7.8 years. A sample of 30 154 individuals underwent cardiopulmonary imaging, physical examinations, routine laboratory tests, questionnaires, and/or functional tests. This study included 24 791 individuals without previous cardiovascular disease for whom high-quality CCTA images were available. Events were followed up via registers until September 2024. The information used from the CCTA images was the extent of coronary atherosclerosis (segment involvement score), presence of noncalcified atherosclerosis, and presence of coronary obstructive disease (stenosis ≥50%). The outcome was a composite of first occurrence of nonfatal myocardial infarction or death from coronary heart disease. During follow-up, 304 coronary events occurred. Segment involvement scores of 3 to 4 and greater than 4 and presence of noncalcified atherosclerosis were associated with hazard ratios of 2.71 (95% CI, 1.34-5.44), 5.27 (95% CI, 2.50-11.07), and 1.66 (95% CI, 1.23-2.22), respectively. In a model based on the PCE and CACS, CCTA-derived data improved risk discrimination (C statistic improved from 0.764 to 0.779; P = .004) and risk reclassification (net reclassification improvement of 0.133 [95% CI, 0.031-0.165]), conferred a net correct upward reclassification of 14.2% in those with events and incorrectly classified 1.6% of participants not experiencing an event into a higher-risk category. Because of the low event rate in the cohort, reclassification mainly occurred in the group classified as at low risk (<5%) according to the PCE. Information on coronary atherosclerosis from CCTA modestly improved risk prediction beyond traditional risk factors and CACS in identifying individuals at risk of coronary events and in need of primary prevention.
In the phase III LAURA study, osimertinib after definitive chemoradiotherapy (CRT) demonstrated a statistically significant, clinically meaningful progression-free survival (PFS) benefit over placebo in patients with unresectable stage III epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC). Understanding real-world (rw) treatment patterns and clinical outcomes can help to measure the impact of new treatments. We report final results from a global, retrospective rw study of patients with unresectable stage III EGFR-mutated NSCLC treated with CRT. Data were extracted from medical records of adults with unresectable stage III EGFR-mutated (Ex19del/L858R) NSCLC, diagnosed January 2016-December 2019, who received CRT as standard of care. The primary outcome was rwPFS. Secondary outcomes included mutation testing patterns and treatment patterns, rw time to next treatment or death (rwTTNTD) and overall survival (OS). Analyses are descriptive; time-to-event outcomes were estimated using Kaplan-Meier methods. Data were included from 172 patients; 59 % of patients harbored Ex19del and 41 % L858R; 76 % received concurrent CRT and 24 % sequential CRT. Overall, 78 %, 18 %, 3 %, and 1 % of patients received CRT alone, CRT plus durvalumab, CRT plus an EGFR-tyrosine kinase inhibitor (TKI) and CRT plus pembrolizumab, respectively, as their first treatment. Of patients who received subsequent treatment (n = 115), most received EGFR-TKIs (75 %; n = 86/115). In patients who received CRT alone as first treatment, median (95 % confidence interval) rwPFS, rwTTNTD, and OS were 6.7 (6.0-9.0), 11.4 (9.0-14.4), and 68.6 (60.9-not evaluable) months, respectively. In this rw study in patients with unresectable stage III EGFR-mutated NSCLC, CRT alone was the most common first treatment and EGFR-TKIs were the most common first subsequent treatment. OS was substantial despite relatively short rwPFS, which may be attributed to subsequent EGFR-TKIs. The findings highlight the unmet need for alternative treatments in this setting.
To co-create a community-informed model of EV adoption, we conducted one English-speaking and two Spanish-speaking focus groups with 29 residents from six disadvantaged urban communities in Southeast Los Angeles. Participants were asked whether they owned an EV or hybrid vehicle, benefits and obstacles to EV ownership, and recommendations for making EV adoption feasible and acceptable. A Community Advisory Council participated in preparation of an interview guide and a review of findings. Social, environmental and personal benefits were cited as reasons for EV ownership but were considered secondary to cost, limited infrastructure (e.g., chargers), and lack of information. This information was used to generate a logic model listing adoption determinants, strategies, causal mechanisms and outcomes. A community informed model serves as a potential tool for promoting the adoption of EVs in disadvantaged communities and creating the conditions necessary for such adoption to be perceived by residents as acceptable, feasible, and appropriate.
To evaluate whether large language models (LLMs) can automate chart review to identify tumor necrosis factor inhibitor (TNFi) switching patterns and reasons for switching in a large real-world cohort. We conducted an observational study using de-identified electronic health record (EHR) data from 2012 to 2023 at a single academic medical center (University of California, San Francisco). TNFi medication orders and linked clinical notes were extracted, requiring at least 6 months of follow-up to identify treatment switches, defined as a change from one TNFi to another at consecutive encounters. Using GPT-4, we extracted which TNFi was stopped and started and classified the reason for switching. Performance was benchmarked against eight open-source LLMs, structured EHR data, and expert annotation. A total of 9187 patients (mean [SD] age, 39.9 [19.0] years; 57.1% female) received ≥1 TNFi with sufficient follow-up. We identified 3104 TNFi switches among 2112 patients. GPT-4 achieved micro-F1 scores of 0.75 (stopped drug), 0.80 (started drug), and 0.83 (reason). Among open-source models, Starling-7B-beta and Llama-3-8B performed most competitively. The most common reason identified by GPT-4 was lack of efficacy (56.9%), followed by adverse events (13.5%) and insurance/cost (10.8%). Both GPT-4 and locally deployable LLMs effectively extracted complex treatment trajectories and rationale from clinical notes, supporting their broader utility in scalable EHR review and real-world evidence generation.
Diverse racial and ethnic representation in clinical trials has been limited, not representative of the US population, and the subject of pending US Food and Drug Administration guidance. Psoriasis presentation and disease burden can vary by skin pigmentation, race and ethnicity, and socioeconomic differences. Overall, there are limited primary data on clinical response, genetics, and quality of life in populations with psoriasis and skin of color (SoC). The Varying Skin Tones in Body and Scalp Psoriasis: Guselkumab Efficacy and Safety trial (VISIBLE) is underway and uses strategies aimed at addressing this persistent gap. To assess the innovative strategies used in the VISIBLE trial to recruit and retain diverse participants in a randomized clinical trial of psoriasis in participants with SoC. This was an ad hoc quality improvement assessment of participant recruitment and retention approaches used by the VISIBLE trial. VISIBLE enrolled and randomized 211 participants (mean [SD] age, 43 [13] years; 75 females [36%] and 136 males [64%]) with SoC and moderate to severe plaque psoriasis from August 2022 to March 2023 to evaluate guselkumab treatment. The self-identified race and ethnicity of the participants was: 1 American Indian/Alaska Native (0.5%), 63 Asian (29.9%), 24 Black (11.4%), 94 Hispanic/Latino (44.5%), 13 Middle Eastern (6.2%), 1 Pacific Islander/Native Hawaiian (0.5%), 12 multiracial (5.7%), and 3 of other race and/or ethnicity (1.4%). Using a combination of objective (colorimetry to determine Fitzpatrick skin type) and self-reported (race and ethnicity consistent with SoC) parameters, VISIBLE sought to broaden inclusion of participants from various backgrounds. Observed improvements were that participant enrollment occurred approximately 7 times faster than anticipated (vs historical recruitment data for psoriasis studies); 211 participants (100%) self-identified themselves as a race or ethnicity other than White; and more than 50% had skin tone in the darker half of the Fitzpatrick skin type spectrum (type IV-VI). Innovations implemented by VISIBLE were (1) assessment of the natural history of postinflammatory pigment alteration and improvements over time using combined objective colorimetry and clinician- and patient-reported outcomes; (2) evaluation of genetic and comorbidity biomarkers relevant to participants with SoC; (3) a diverse demographic-driven approach to site selection (emphasizing investigator and staff diversity and experience with populations with SoC); (4) provision of cultural competency training to enhance participant enrollment and retention; (5) collection of patient-reported outcomes data in participants' primary language; and (6) periodic, blinded central review and feedback on investigator efficacy scoring to promote consistency and accuracy in evaluating psoriasis in participants with SoC. VISIBLE is a unique study focused on addressing important knowledge and data gaps in populations of patients with psoriasis and SoC, with the goal of generating data to help improve clinical care and inform future best practices in diversity within dermatology research. The rapid study enrollment demonstrates that intentional and strategic approaches to clinical trial design and conduct can speed recruitment and bolster participation and retention of diverse populations in a dermatologic setting. ClinicalTrials.gov Identifier: NCT05272150.
Although warning systems in connected environments have become increasingly common, their psychological impact on driving confidence remains underexplored. This study aims to analyze driving confidence under hazardous road events-such as emergency braking of front vehicles (EB-FV), work zones (WZ), and tunnels (Tun)-in response to warning systems, using a connected simulation platform. By integrating traffic psychology using hazardous event warnings with connected-vehicle technology, a unique perspective that has not been covered in previous studies is provided. Driving confidence was quantified using driving simulation technology in two dimensions: speed performance and driving operations. The results show that predictive warning systems significantly improve driver confidence and control. Specifically, in the Tun, compared to the no-warning condition, the average driving speed decreased by 16.38%, and speed variability StdV decreased by 27.75%. Additionally, steering control was more stable, with a 18.40% decrease in steering wheel angle variability (SDSA) in the EB-FV scenario, and 7.31% in the WZ scenario. Additionally, the study highlights a significant improvement in driver confidence when warning information is provided. The conclusions are particularly applicable to structured road environments with reliable V2X communication and assume that drivers have some degree of familiarity with connected systems. This study provides theoretical and practical insights into the design of adaptive warning strategies for future intelligent transportation systems.
The introduction of large language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4; OpenAI), has generated significant interest in health care, yet studies evaluating their performance in a clinical setting are lacking. Determination of clinical acuity, a measure of a patient's illness severity and level of required medical attention, is one of the foundational elements of medical reasoning in emergency medicine. To determine whether an LLM can accurately assess clinical acuity in the emergency department (ED). This cross-sectional study identified all adult ED visits from January 1, 2012, to January 17, 2023, at the University of California, San Francisco, with a documented Emergency Severity Index (ESI) acuity level (immediate, emergent, urgent, less urgent, or nonurgent) and with a corresponding ED physician note. A sample of 10 000 pairs of ED visits with nonequivalent ESI scores, balanced for each of the 10 possible pairs of 5 ESI scores, was selected at random. The potential of the LLM to classify acuity levels of patients in the ED based on the ESI across 10 000 patient pairs. Using deidentified clinical text, the LLM was queried to identify the patient with a higher-acuity presentation within each pair based on the patients' clinical history. An earlier LLM was queried to allow comparison with this model. Accuracy score was calculated to evaluate the performance of both LLMs across the 10 000-pair sample. A 500-pair subsample was manually classified by a physician reviewer to compare performance between the LLMs and human classification. From a total of 251 401 adult ED visits, a balanced sample of 10 000 patient pairs was created wherein each pair comprised patients with disparate ESI acuity scores. Across this sample, the LLM correctly inferred the patient with higher acuity for 8940 of 10 000 pairs (accuracy, 0.89 [95% CI, 0.89-0.90]). Performance of the comparator LLM (accuracy, 0.84 [95% CI, 0.83-0.84]) was below that of its successor. Among the 500-pair subsample that was also manually classified, LLM performance (accuracy, 0.88 [95% CI, 0.86-0.91]) was comparable with that of the physician reviewer (accuracy, 0.86 [95% CI, 0.83-0.89]). In this cross-sectional study of 10 000 pairs of ED visits, the LLM accurately identified the patient with higher acuity when given pairs of presenting histories extracted from patients' first ED documentation. These findings suggest that the integration of an LLM into ED workflows could enhance triage processes while maintaining triage quality and warrants further investigation.
Durvalumab improves survival when used as consolidation therapy after chemoradiation (CRT) in patients with stage III NSCLC. The optimal consolidation therapy for patients with EGFR-mutant (EGFRmut) stage III NSCLC remains unknown. In this multi-institutional, international retrospective analysis across 24 institutions, we evaluated outcomes in patients with stage III EGFRmut NSCLC treated with concurrent CRT followed by consolidation therapy with osimertinib, durvalumab, or observation between 2015 and 2022. Kaplan-Meier method was used to estimate real-world progression-free survival (rwPFS, primary end point) and overall survival (secondary end point). Treatment-related adverse events (trAEs) during consolidation treatment were defined using Common Terminology Criteria for Adverse Events version 5.0. Multivariable Cox regression analysis was used. Of 136 patients with stage III EGFRmut NSCLC treated with definitive concurrent CRT, 56 received consolidation durvalumab, 33 received consolidation osimertinib, and 47 was on observation alone. Baseline characteristics were similar across the three cohorts. With a median follow-up of 46 months for the entire cohort, the median duration of treatment was not reached (NR) for osimertinib (interquartile range: NR-NR) and was 5.5 (interquartile range: 2.4-10.8) months with durvalumab. After adjusting for nodal status, stage III A/B/C, and age, patients treated with consolidation osimertinib had significantly longer 24-month rwPFS compared to those treated with durvalumab or in the observation cohorts (osimertinib: 86%, durvalumab: 30%, observation: 27%, p < 0.001 for both comparisons). There was no difference in rwPFS between the durvalumab and the observation cohorts. No significant difference in overall survival across the three cohorts was detected, likely due to the limited follow-up. Any-grade trAE occurred in 52% (2 [6.1%] grade ≥3) and 48% (10 [18%] grade ≥3) of patients treated with osimertinib and durvalumab, respectively. Of 45 patients who progressed on consolidation durvalumab, 37 (82%) subsequently received EGFR tyrosine kinase inhibitors. Of these, 14 (38%) patients developed trAEs including five patients with pneumonitis (14%; 2 [5.4%] grade ≥3) and five patients with diarrhea (14%; 1 [2.7%] grade ≥3). This study suggests that among patients with stage III unresectable NSCLC with a sensitizing EGFR mutation, consolidation osimertinib was associated with a significantly longer rwPFS compared to durvalumab or observation. No unanticipated safety signals were observed with consolidation osimertinib.
Digital therapeutics (DTx) are a somewhat novel class of US Food and Drug Administration-regulated software that help patients prevent, manage, or treat disease. Here, we use natural language processing to characterise registered DTx clinical trials and provide insights into the clinical development landscape for these novel therapeutics. We identified 449 DTx clinical trials, initiated or expected to be initiated between 2010 and 2030, from ClinicalTrials.gov using 27 search terms, and available data were analysed, including trial durations, locations, MeSH categories, enrolment, and sponsor types. Topic modelling of eligibility criteria, done with BERTopic, showed that DTx trials frequently exclude patients on the basis of age, comorbidities, pregnancy, language barriers, and digital determinants of health, including smartphone or data plan access. Our comprehensive overview of the DTx development landscape highlights challenges in designing inclusive DTx clinical trials and presents opportunities for clinicians and researchers to address these challenges. Finally, we provide an interactive dashboard for readers to conduct their own analyses.
Kidney transplant is not only the best treatment for patients with advanced kidney disease but it also reduces health care expenditure. The management of transplant patients is complex as they require special care by transplant nephrologists who have expertise in assessing transplant candidates, understand immunology and organ rejection, have familiarity with perioperative complications, and have the ability to manage the long-term effects of chronic immunosuppression. This skill set at the intersection of multiple disciplines necessitates additional training in Transplant Nephrology. Currently, there are more than 250,000 patients with a functioning kidney allograft and over 100,000 waitlisted patients awaiting kidney transplant, with a burgeoning number added to the kidney transplant wait list every year. In 2022, more than 40,000 patients were added to the kidney wait list and more than 25,000 received a kidney transplant. The Advancing American Kidney Health Initiative, passed in 2019, is aiming to double the number of kidney transplants by 2030 creating a need for additional transplant nephrologists to help care for them. Over the past decade, there has been a decline in the Nephrology-as well Transplant Nephrology-workforce due to a multitude of reasons. The American Society of Transplantation Kidney Pancreas Community of Practice created a workgroup to discuss the Transplant Nephrology workforce shortage. In this article, we discuss the scope of the problem and how the Accreditation Council for Graduate Medical Education recognition of Transplant Nephrology Fellowship could at least partly mitigate the Transplant Nephrology work force crisis.
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Alternative solid electrolytes are the next key step in advancing lithium batteries with better thermal and chemical stability. A soft solid electrolyte, (Adpn)2LiPF6 (Adpn, adiponitrile), is synthesized and characterized that exhibits high thermal and electrochemical stability and good ionic conductivity, overcoming several limitations of conventional organic and ceramic materials. The surface of the electrolyte possesses a liquid nano-layer of Adpn that links grains for a facile ionic conduction without high pressure/temperature treatments. Further, the material can quickly self-heal if fractured and provides liquid-like conduction paths via the grain boundaries. A substantially high ion conductivity (~10-4 S cm-1) and lithium-ion transference number (0.54) are obtained due to weak interactions between 'hard' (charge dense) Li+ ions and the 'soft' (electronically polarizable) -C≡N group of Adpn. Molecular simulations predict that Li+ ions migrate at the co-crystal grain boundaries with a (preferentially) lower activation energy Ea and within the interstitial regions between the co-crystals with higher Ea values, where the bulk conductivity is a smaller but extant contribution. These co-crystals establish a special concept of crystal design to increase the thermal stability of LiPF6 by separating ions in the Adpn solvent matrix, and also exhibit a unique mechanism of ion conduction via low-resistance grain boundaries, which contrasts with ceramics or gel electrolytes.
The transition to electric vehicles is projected to have considerable public health co-benefits, but most evidence regarding air quality and health impacts comes from projections rather than real-world data. We evaluated whether population-level respiratory health and air quality co-benefits were already detectable at the relatively low levels of zero-emissions vehicles (ZEVs: battery electric, plug-in hybrid, hydrogen fuel cell vehicle) adoption in California, and evaluated the ZEV adoption gap in underserved communities. We conducted a zip code-level ecologic study relating changes in annual number of ZEVs (nZEV) per 1000 population from 2013 to 2019 to: (i) annual average monitored nitrogen dioxide (NO2) concentrations and (ii) annual age-adjusted asthma-related emergency department (ED) visit rates, while considering educational attainment. The average nZEV increased from 1.4 per 1000 population in 2013 (standard deviation [SD]: 2.1) to 14.7 per 1000 in 2019 (SD: 14.7). ZEV adoption was considerably slower in zip codes with lower educational attainment (p < 0.0001). A within-zip code increase of 20 ZEVs per 1000 was associated with a - 0.41 ppb change in annual average NO2 (95 % confidence interval [CI]:-1.12, 0.29) in an adjusted model. A within-zip code increase of 20 ZEVs per 1000 population was associated with a 3.2 % decrease in annual age-adjusted rate of asthma-related ED visits (95 % CI:-5.4, -0.9). Findings were supported by a variety of sensitivity analyses. Observational data on the early phase ZEV transition in California provided a natural experiment, enabling us to document the first real-world associations between increasing nZEV and changes in air quality and health. Results suggest co-benefits of the early-phase transition to ZEVs but with an adoption gap among populations with lower socioeconomic status which threatens the equitable distribution of possible co-benefits.
Hypertensive disorders of pregnancy complicate up to 10% of pregnancies and remain the major cause of maternal and neonatal morbidity and mortality. Hypertensive disorders of pregnancy can be classified into four groups depending on the onset of hypertension and the presence of target organ involvement: chronic hypertension, preeclampsia, gestational hypertension, and superimposed preeclampsia on chronic hypertension. Hypertension during pregnancy is associated with a higher risk of cardiovascular disease and kidney failure. Early diagnosis and proper treatment for pregnant women with hypertension remain a priority since this leads to improved maternal and fetal outcomes. Labetalol, nifedipine, methyldopa, and hydralazine are the preferred medications to treat hypertension during pregnancy. In this comprehensive review, we discuss the diagnostic criteria, evaluation, and management of pregnant women with hypertension.
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This article describes a new and innovative training program to assist family physicians to better care for their patients with mental health conditions. Trained family physician leaders train other family physicians. The training package includes a wide range of tools that can be used by physicians in their own offices. Preliminary results indicate that physicians want to be trained, and data indicate a high degree of success for the training module. Some 91% of physicians who attended the training indicated that it had improved their practice, and 94% indicated that it had improved patient care. The training materials are online for those who wish to learn more.