Clinical decision support for pharmacologic management of treatment-resistant depression with augmented large language models
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Le résumé fourni par la source
Background: We evaluated whether a large language model could assist in selecting psychopharmacological treatments for adults with treatment-resistant depression. Methods: We generated 20 clinical vignettes reflecting treatment-resistant depression among adults based on distributions drawn from electronic health records. Each vignette was evaluated by 2 expert psychopharmacologists to determine and rank the 5 best next-step pharmacologic interventions, as well as contraindicated or poor next-step treatments. Vignettes were then presented in random order, permuting gender and race, to a large language model (Qwen 2.5:7B), augmented with a synopsis of published treatment guidelines. Model output was compared to expert rankings, as well as to those of a convenience sample of community clinicians and an additional group of expert clinicians. Results: The augmented model prioritized the expert-designated optimal choice for 114/320 vignettes (35.6 %, 95 % CI 30.6 %-41.0 %; Cohen's kappa = 0.34, 95 % CI 0.28-0.39). There were no vignettes for which any of the model choices were among the poor or contraindicated treatments. Results were not meaningfully different when gender or race of the vignette was permuted to examine risk for bias. A sample of community clinicians identified the optimal treatment choice for 12/91 vignettes (13.2 %, 95 % CI: 7.7-21.6 %; Cohen's kappa = 0.10, 95 % CI 0.03-0.18), while an additional group of expert psychopharmacologists identified optimal treatment for 9/140 (6.4 %, 95 %CI: 3.4-11.8 %; Cohen's kappa = 0.03, 95 % CI 0.01-0.08). Conclusion: An augmented language model demonstrated moderate agreement with expert recommendations and avoided contraindicated treatments, suggesting potential as a tool for supporting complex psychopharmacologic decision-making in treatment-resistant depression.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Clinical decision support for pharmacologic management of treatment-resistant depression with augmented large language models
- Date Crossref
- 01/12/2025
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Harvard University pays non établi dans la noticeUniversité ou école supérieure
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Massachusetts General Hospital Center for Quantitative Health and Department of Psychiatry pays non établi dans la noticeÉtablissement de santé
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Quantitative BioSciences pays non établi dans la noticeOrganisation à but non lucratif
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Stanford University Department of Psychiatry pays non établi dans la noticeUniversité ou école supérieure
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The University of Sydney pays non établi dans la noticeUniversité ou école supérieure
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Royal North Shore Hospital pays non établi dans la noticeÉtablissement de santé
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Northern Sydney Local Health District pays non établi dans la noticeOrganisme public
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Wellcome Centre for Ethics and Humanities pays non établi dans la noticeStructure de recherche
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National Institute of Mental Health Section on the Neurobiology and Treatment of Mood Disorders pays non établi dans la noticeStructure de recherche
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University of Alabama at Birmingham pays non établi dans la noticeUniversité ou école supérieure
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Nathan Kline Institute for Psychiatric Research pays non établi dans la noticeOrganisation à but non lucratif
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University of New Mexico Department of Psychiatry and Behavioral Sciences pays non établi dans la noticeUniversité ou école supérieure
Harvard University, Center for Quantitative Health and Department of Psychiatry — Massachusetts General Hospital et Quantitative BioSciences, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.