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Introduction: Personal statements can be decisive in Canadian residency applications. With the rise in AI technology, ethical concerns regarding authenticity and originality become more pressing. This study explores the capability of ChatGPT in producing personal statements for plastic surgery residency that match the quality of statements written by successful applicants. Methods: ChatGPT was utilized to generate a cohort of personal statements for CaRMS (Canadian Residency Matching Service) to compare with previously successful Plastic Surgery applications. Each AI-generated and human-written statement was randomized and anonymized prior to assessment. Two retired members of the plastic surgery residency selection committee from the University of British Columbia, evaluated these on a 0 to 10 scale and provided a binary response judging whether each statement was AI or human written. Statistical analysis included Welch 2-sample t tests and Cohen's Kappa for agreement. Results: Twenty-two personal statements (11 AI-generated by ChatGPT and 11 human-written) were evaluated. The overall mean scores were 7.48 (SD 0.932) and 7.68 (SD 0.716), respectively, with no significant difference between AI and human groups (P = .4129). The average accuracy in distinguishing between human and AI letters was 65.9%. The Cohen's Kappa value was 0.374. Conclusions: ChatGPT can generate personal statements for plastic surgery residency applications with quality indistinguishable from human-written counterparts, as evidenced by the lack of significant scoring difference and moderate accuracy in discrimination by experienced surgeons. These findings highlight the evolving role of AI and the need for updated evaluative criteria or guidelines in the residency application process. Introduction: Les lettres personnelles peuvent être décisives lors des candidatures canadiennes à la résidence. Étant donné l’intérêt pour la technologie d’IA, les préoccupations éthiques au sujet de l’authenticité et de l’originalité deviennent plus urgentes. La présente étude vise à établir si ChatGPT peut produire des lettres personnelles pour la résidence en chirurgie plastique qui correspondent à la qualité de celles rédigées par des candidats retenus. Méthodologie: ChatGPT a été utilisé pour produire une cohorte de lettres personnelles destinées au CaRMS (Service canadien de jumelage des résidents) afin de les comparer à celles de candidatures en chirurgie plastique retenues par le passé. Chaque lettre produite par l’IA et rédigée par un humain a été randomisée et anonymisée avant l’évaluation. Deux membres retraités du comité de sélection à la résidence chirurgie plastique de l’Université de la Colombie-Britannique ont évalué les lettres sur une échelle de 0 à 10 et ont donné une réponse binaire pour établir si chacune était produite par l’IA ou rédigée par un humain. L’analyse statistique incluait des tests à deux échantillons de Welch et le coefficient Kappa de Cohen pour en vérifier la concordance. Résultats: Les évaluateurs ont examiné 22 lettres personnelles (11 produites par IA—ChatGPT—et 11 rédigées par un humain). Les scores moyens globaux s’élevaient à 7,48 (ÉT 0932) et 7,68 (ÉT 0716), respectivement, sans différence importante entre le groupe d’IA et le groupe humain (p = 0,4129). L’exactitude moyenne à distinguer les lettres rédigées par des humains de celles produites par l’IA atteignait 65,9%. La valeur Kappa de Cohen était de 0374. Conclusions: ChatGPT peut produire des lettres personnelles pour les candidatures à la résidence en chirurgie plastique, d’une qualité indissociable de celles rédigées par des humains, comme le démontre le peu de différence entre les scores et l’exactitude modérée de la discrimination par des chirurgiens d’expérience. Ces observations démontrent le rôle évolutif de l’IA et la nécessité de mettre à jour les critères ou les directives d’évaluation lors du processus de demande en résidence.

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Already rocked by decades of political interference, corporate influence, mismanagement, and partisan efforts to undermine its authority, the expert bureaucracy, the "lifeblood" of the US administrative state, is now gasping for air. On 23 May, President Trump issued an executive order (EO)-Restoring Gold Standard Science-promising to fix these issues. Instead, the EO is poised to make them far worse: It officially empowers political appointees to override conclusions and interpretations of government scientists, threaten their professional autonomy, and undermine the scientific capacity of research and regulatory agencies.

Dementia-friendly hospitals are increasingly discussed in healthcare, politics, research and society as a way to improve hospital stays for people with dementia. However, the perspective of people with dementia is often underrepresented, and current definitions and concepts are primarily based on the expertise of healthcare professionals. To identify characteristics of a dementia-friendly hospital from the perspective of people with dementia. A qualitative design embedded in a case study was used. We conducted 15 semi-structured interviews with people with dementia during their hospital stay. The interviews were analysed using reflexive thematic analysis with an inductive approach. In our study, we adopted a participatory approach and actively involved people with dementia and other stakeholders in the development of the interview guide and in the data analysis. The participants expect more than what's necessary from a dementia-friendly hospital, as presented by eight characteristics identified: (i) The most important thing is how we treat each other; (ii) Someone cares about ME; (iii) No hully gully! I want professional care; (iv) Focusing on my goals; (v) LOOK! I've still got capabilities; (vi) Skipping Groundhog Day; (vii) A bit of both: mingle and single, and (viii) Recognising what relatives mean to me. Our results highlight the importance of involving people with dementia in the development of dementia-friendly hospitals as only they have the lived experience and a different perspective from that of healthcare professionals.

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The US Department of Health and Human Services’ proposed imposition of placebo-controlled trials for testing most new and existing vaccines jeopardizes public health and puts trial participants at unnecessary and easily avoidable risks—a violation of both scientific and ethical standards. [Image: see text]

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Confirmation bias (CB) is a cognitive bias that allows us to fool ourselves by selectively filtering data and distorting analyses to support favored beliefs or hypotheses. In this article, I will briefly review some classic experiments from cognitive psychology that illustrate what a powerful, pernicious, and insidious force CB is. I will then discuss how to recognize CB in our own thinking and behavior and describe specific elements of good experimental design that can mitigate its effects. These elements-such as randomization and blinding-are conceptually straightforward but often difficult in practice and therefore not as widely implemented as they should be.

Don’t fool yourself to think that you are safe from emails just because you enabled the “Out of office” function. It is not an ‘Off’ button that grants you reprieve from daily chores. [Image: see text]

Cytoplasmic condensates that mimic nuclear pore complexes entice viruses away from the nucleus.

The potential capabilities of generative artificial intelligence (AI) tools have been relatively unexplored, particularly in the realm of creating personalized statements for medical students applying to residencies. This study aimed to investigate the ability of generative AI, specifically ChatGPT and Google BARD, to generate personal statements and assess whether faculty on residency selection committees could (1) evaluate differences between real and AI statements and (2) determine differences based on 13 defined and specific metrics of a personal statement. Fifteen real personal statements were used to generate 15 unique and distinct personal statements from ChatGPT and BARD each, resulting in a total of 45 statements. Statements were then randomized, blinded, and presented to a group of faculty reviewers on residency selection committees. Reviewers assessed the statements by 14 metrics including if the personal statement was AI-generated or real. Comparison of all metrics was performed. Faculty correctly identified 88% (79/90) real statements, 90% (81/90) BARD, and 44% (40/90) ChatGPT statements. Accuracy of identifying real and BARD statements was 89%, but this dropped to 74% when including ChatGPT. In addition, the accuracy did not increase as faculty members reviewed more personal statements (area under the curve [AUC] 0.498, p = 0.966). BARD performed poorer than both real and ChatGPT across all metrics (p < 0.001). Comparing real with ChatGPT, there was no difference in most metrics, except for Personal Interests, Reasons for Choosing Residency, Career Goals, Compelling Nature and Originality, and all favoring the real personal statements (p = 0.001, p = 0.002, p < 0.001, p < 0.001, and p < 0.001, respectively). Faculty members accurately identified real and BARD statements, but ChatGPT deceived them 56% of the time. Although AI can craft convincing statements that are sometimes indistinguishable from real ones, replicating the humanistic experience, personal nuances, and individualistic elements found in real personal statements is difficult. Residency selection committees might want to prioritize these particular metrics while assessing personal statements, given the growing capabilities of AI in this arena. Residency selection committees may want to prioritize certain metrics unique to the human element such as personal interests, reasons for choosing residency, career goals, compelling nature, and originality when evaluating personal statements.

Fine-needle aspiration cytology (FNAC) is often used as a screening tool. Cytopathologist should be aware of various mimickers of the malignancy. One of these is infarction of benign breast lesions. Careful examination of cytomorphological features will avoid the misdiagnosis of malignancy in such cases. Six cases were diagnosed as benign breast lesion for 4 years and 5 months in our newly established tertiary referral center. Histopathology follow-up was available in one case. Of six cases, three cases (50%) were reported as fibroadenoma with infarction, two cases (33%) as benign breast lesion with infarction favor fibroadenoma, and one as breast lesion with infarction favor phyllodes tumor in view of recurrence. Most were in the second or third decade of their life. All cases showed dyscohesive cells with pyknotic nuclei. Monolayered sheets of necrotic cells were seen in the four cases (66%). Viable cells were seen in four cases. FNAC provides a rapid and accurate diagnosis of benign breast lesions with infarction in the hands of experienced pathologists who can help in better patient care. Small-sized uniform pyknotic nuclei of dyscohesive cells and regular nuclear membranes help to differentiate them from inflammatory and malignant lesions.