Explore the vast range of titles available.

Large language model (LLM)-based artificial intelligence chatbots direct the power of large training data sets toward successive, related tasks as opposed to single-ask tasks, for which artificial intelligence already achieves impressive performance. The capacity of LLMs to assist in the full scope of iterative clinical reasoning via successive prompting, in effect acting as artificial physicians, has not yet been evaluated. This study aimed to evaluate ChatGPT's capacity for ongoing clinical decision support via its performance on standardized clinical vignettes. We inputted all 36 published clinical vignettes from the Merck Sharpe & Dohme (MSD) Clinical Manual into ChatGPT and compared its accuracy on differential diagnoses, diagnostic testing, final diagnosis, and management based on patient age, gender, and case acuity. Accuracy was measured by the proportion of correct responses to the questions posed within the clinical vignettes tested, as calculated by human scorers. We further conducted linear regression to assess the contributing factors toward ChatGPT's performance on clinical tasks. ChatGPT achieved an overall accuracy of 71.7% (95% CI 69.3%-74.1%) across all 36 clinical vignettes. The LLM demonstrated the highest performance in making a final diagnosis with an accuracy of 76.9% (95% CI 67.8%-86.1%) and the lowest performance in generating an initial differential diagnosis with an accuracy of 60.3% (95% CI 54.2%-66.6%). Compared to answering questions about general medical knowledge, ChatGPT demonstrated inferior performance on differential diagnosis (β=-15.8%; P<.001) and clinical management (β=-7.4%; P=.02) question types. ChatGPT achieves impressive accuracy in clinical decision-making, with increasing strength as it gains more clinical information at its disposal. In particular, ChatGPT demonstrates the greatest accuracy in tasks of final diagnosis as compared to initial diagnosis. Limitations include possible model hallucinations and the unclear composition of ChatGPT's training data set.

We revisit and extend a variety of inequalities related to power weighted Rellich and Hardy–Rellich inequalities, including an inequality due to Schmincke.

The principal aim of this paper is to extend Birman’s sequence of integral inequalities originally obtained in Mat. Sb. (N.S.) 55 (97), 125–174, (1961), and containing Hardy’s and Rellich’s inequality as special cases, to a sequence of inequalities that incorporates power weights x α for x varying in intervals ( 0 , ρ ) , ρ ∈ ( 0 , ∞ ) ∪ { ∞ } , on either side and logarithmic refinements on the right-hand side of the inequality as well. Employing a new technique of proof relying on a combination of transforms originally due to Hartman and Müller-Pfeiffer, the parameter α ∈ R in the power weights is now unrestricted, considerably improving on prior results in the literature. We also discuss optimality of the constants in these inequalities. This continues a tradition of logarithmic refinements in connection with Hardy’s inequality, going back to work in oscillation theory by Kneser (Math. Ann. 42 , 409–435, (1893)), Hartman (Am. J. Math. 70 , 764-779 (1948)), Hille (Trans. Am. Math. Soc. 64 , 234-252 (1948)), and Rellich (Math. Ann. 122 , 343–368 (1951)), resulting in a sequence of sharp statements of boundedness from below by zero of a class of homogeneous 2 m th order differential operators on C 0 ∞ ( ( 0 , ρ ) ) . We also prove the analogous inequalities on exterior intervals, that is, for f ∈ C 0 ∞ ( ( ρ , ∞ ) ) . Finally, we also indicate a vector-valued version of these inequalities, replacing complex-valued f ( · ) by f ( · ) ∈ H , with H a complex, separable Hilbert space.

Polypharmacy remains an important challenge for patients with extensive medical complexity. Given the primary care shortage and the increasing aging population, effective polypharmacy management is crucial to manage the increasing burden of care. The capacity of large language model (LLM)-based artificial intelligence to aid in polypharmacy management has yet to be evaluated. Here, we evaluate ChatGPT’s performance in polypharmacy management via its deprescribing decisions in standardized clinical vignettes. We inputted several clinical vignettes originally from a study of general practicioners’ deprescribing decisions into ChatGPT 3.5, a publicly available LLM, and evaluated its capacity for yes/no binary deprescribing decisions as well as list-based prompts in which the model was prompted to choose which of several medications to deprescribe. We recorded ChatGPT responses to yes/no binary deprescribing prompts and the number and types of medications deprescribed. In yes/no binary deprescribing decisions, ChatGPT universally recommended deprescribing medications regardless of ADL status in patients with no overlying CVD history; in patients with CVD history, ChatGPT’s answers varied by technical replicate. Total number of medications deprescribed ranged from 2.67 to 3.67 (out of 7) and did not vary with CVD status, but increased linearly with severity of ADL impairment. Among medication types, ChatGPT preferentially deprescribed pain medications. ChatGPT’s deprescribing decisions vary along the axes of ADL status, CVD history, and medication type, indicating some concordance of internal logic between general practitioners and the model. These results indicate that specifically trained LLMs may provide useful clinical support in polypharmacy management for primary care physicians.

Background Building healthcare service and health professionals’ capacity and capability to rapidly translate research evidence into health practice is critical to the effectiveness and sustainability of healthcare systems. This review scoped the literature describing programmes to build knowledge translation capacity and capability in health professionals and healthcare services, and the evidence supporting these. Methods This scoping review was undertaken using the Joanna Briggs Institute scoping review methodology. Four research databases (Ovid MEDLINE, CINAHL, Embase, and PsycInfo) were searched using a pre-determined strategy. Eligible studies described a programme implemented in healthcare settings to build health professional or healthcare service knowledge translation capacity and capability. Abstracts and full texts considered for inclusion were screened by two researchers. Data from included papers were extracted using a bespoke tool informed by the scoping review questions. Results Database searches yielded 10,509 unique citations, of which 136 full texts were reviewed. Thirty-four papers were included, with three additional papers identified on citation searching, resulting in 37 papers describing 34 knowledge translation capability building programmes. Programmes were often multifaceted, comprising a combination of two or more strategies including education, dedicated implementation support roles, strategic research-practice partnerships and collaborations, co-designed knowledge translation capability building programmes, and dedicated funding for knowledge translation. Many programmes utilised experiential and collaborative learning, and targeted either individual, team, organisational, or system levels of impact. Twenty-seven programmes were evaluated formally using one or more data collection methods. Outcomes measured varied significantly and included participant self-reported outcomes, perceived barriers and enablers of knowledge translation, milestone achievement and behaviour change. All papers reported that programme objectives were achieved to varying degrees. Conclusions Knowledge translation capacity and capability building programmes in healthcare settings are multifaceted, often include education to facilitate experiential and collaborative learning, and target individual, team, organisational, or supra-organisational levels of impact. Although measured differently across the programmes, the outcomes were positive. The sustainability of programmes and outcomes may be undermined by the lack of long-term funding and inconsistent evaluation. Future research is required to develop evidence-informed frameworks to guide methods and outcome measures for short-, medium- and longer-term programme evaluation at the different structural levels.

Objectives: Orthopaedic surgeons play a critical role in ensuring the health and safety of professional athletes. Despite the privilege of treating elite athletes, there exists great financial exposure to individual physicians in the event of a malpractice lawsuit. The purpose of this study was to evaluate and model malpractice liability exposure of the sports medicine surgeon caring for athletes in the National Football League(NFL), Major League Baseball(MLB), and National Hockey League(NHL) with respect to player position and additional supplemental malpractice insurance needs. We hypothesize that current liability coverage cannot adequately address the demands of caring for elite athletes in major sports leagues. Methods: 2,447 NFL, 992 MLB, and 980 NHL player contracts from the 2022-23 season were aggregated from a publicly available online database. Position, team, total contract value, and average yearly salary were noted. Risk ratios were calculated with respect to $1mm USD and $3mm USD of annual occurrence-based malpractice liability awards and used to generate a “covered-to-treat” analysis. Supplemental malpractice liability insurance was quantified. Results: Assuming $1mm USD and $3mm USD occurrence-based awards covered by malpractice liability insurance, team physicians can fully are financially eligible to treat 17.3% and 50.0% of NFL players, 43.2% and 59.7% of MLB players, and 13.6% and 41.0% of NHL players, without incurring additional personal financial risk, from a risk-based medicolegal model. Liability policies of $52.6mm USD, $108.1mm USD, and $64.1mm USD are required to treat 95% of NFL, MLB, and NHL players, respectively. Positions carrying the greatest risk ratios are QB(9.9) in the NFL, Right Field(15.1) in the MLB, and Center(5.7) in the NHL. Conclusions: Sports medicine specialists caring for elite athletes face potential personal financial risk due to insufficient medicolegal coverage. While coverage may vary amongst different practice settings including private, academic, or public state institutions, medical malpractice risk is crucial in partnerships between sports franchises, hospitals, players, agents, and physicians to protect sports medicine physicians and offer the highest quality care.

We obtain sharp quantitative bounds which describe how an eigenspace of the Dirichlet Laplacian defined on a smooth domain changes when the smooth domain is replaced by a slightly smaller domain.

We revisit and extend a variety of inequalities related to power weighted Rellich and Hardy–Rellich inequalities, including an inequality due to Schmincke.

Patients with chronic obstructive pulmonary disease (COPD) in pulmonary rehabilitation programs (PRPs) are not routinely screened for dysphagia. An Australian regional health service audit revealed that patients with COPD are frequently referred to speech pathology during acute admissions, rather than proactively to mitigate the risk of dysphagia-related consequences. Referral patterns to speech pathology using a novel transdisciplinary approach for identifying at risk for dysphagia patients in a PRP were explored. The aim of this study was to investigate the impact of a transdisciplinary dysphagia screening questionnaire on speech pathology referrals within a cohort of patients with COPD enrolled in a PRP. This quasi-experimental study introduced a dysphagia screening questionnaire in a PRP using a transdisciplinary approach. A retrospective audit of PRP patients (n = 563) between 01/01/2014 and 31/12/2018 was conducted to identify the frequency of referrals to speech pathology for dysphagia. Data was compared to a cohort of patients (n = 50) enrolled in the PRP (from 01/02/21 to 30/11/21) after introduction of the questionnaire using Fisher’s exact test. Less than 1% (n = 4/563) of PRP patients were referred to speech pathology prior to implementation of the questionnaire. Following the implementation, referrals to speech pathology significantly increased to 16% (8/50) (X 2  = 7.72, P < 0.05; odds ratio = 7.89 95% CI [1.94, 32.1]). Introducing a dysphagia screening questionnaire increased referrals to speech pathology from a PRP. This study demonstrated the potential for a transdisciplinary approach in early screening for patients at risk of dysphagia for patients with COPD. Further research is encouraged to explore patient motivation towards speech pathology input with COPD-related dysphagia and clinicians’ perceived self-efficacy in using the questionnaire.