Moving From Idealism to Realism With Data Sharing
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Résumé
Ideas and OpinionsMarch 2023Moving From Idealism to Realism With Data SharingKeith A. Marsolo, PhD, Kevin P. Weinfurt, PhD, Karen L. Staman, MS, and Bradley G. Hammill, DrPHKeith A. Marsolo, PhDDepartment of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina (K.A.M., K.P.W., K.L.S., B.G.H.)., Kevin P. Weinfurt, PhDDepartment of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina (K.A.M., K.P.W., K.L.S., B.G.H.)., Karen L. Staman, MSDepartment of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina (K.A.M., K.P.W., K.L.S., B.G.H.)., and Bradley G. Hammill, DrPHDepartment of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina (K.A.M., K.P.W., K.L.S., B.G.H.).Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M22-2973 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Significant efforts have been made in the past decade to promote open science and data sharing in clinical research. The moral and scientific arguments are clear: If data are shared, it could promote transparency and understanding of the results, honor the participation of individuals, and enable new discoveries (1).The White House Office of Science and Technology Policy recently updated guidance requiring that results of federally funded research be made immediately available, and federal agencies have drafted a series of policies that outline expectations of their awardees. For example, the National Institutes of Health (NIH) has released a new Policy ...References1. The benefits of data sharing. In: Institute of Medicine. Sharing Clinical Research Data: Workshop Summary. National Academies Pr; 2013. Accessed at www.ncbi.nlm.nih.gov/books/NBK137823 on 9 May 2022. Google Scholar2. National Institutes of Health. Final NIH Policy for Data Management and Sharing. 2022. Accessed at https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-013.html on 11 November 2022. Google Scholar3. Patient-Centered Outcomes Research Institute. Policy for Data Management and Data Sharing. 2018. Accessed at www.pcori.org/about-us/governance/policy-data-management-and-data-sharing on 13 May 2022. Google Scholar4. National Institutes of Health. Supplemental information to the NIH Policy for Data Management and Sharing: protecting privacy when sharing human research participant data. 2022. Accessed at https://grants.nih.gov/grants/guide/notice-files/NOT-OD-22-213.html on 12 December 2022. Google Scholar5. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. [PMID: 26978244] doi:10.1038/sdata.2016.18 CrossrefMedlineGoogle Scholar6. European Medicines Agency. Clinical data publication. 2018. Accessed at www.ema.europa.eu/en/human-regulatory/marketing-authorisation/clinical-data-publication on 19 November 2022. Google Scholar7. Gøtzsche PC, Jørgensen AW. Opening up data at the European Medicines Agency [Letter]. BMJ. 2011;342:d2686. [PMID: 21558364] doi:10.1136/bmj.d2686 CrossrefMedlineGoogle Scholar8. Herder M, Doshi P, Lemmens T. Precedent pushing practice: Canadian court orders release of unpublished clinical trial data. BMJ Opinion. 19 July 2018. Accessed at https://blogs.bmj.com/bmj/2018/07/19/precedent-pushing-practice-canadian-court-orders-release-of-unpublished-clinical-trial-data on 19 November 2022. Google Scholar9. National Institutes of Health. NIH Genomic Data Sharing Policy. 2014. Accessed at https://grants.nih.gov/grants/guide/notice-files/not-od-14-124.html on 12 December 2022. Google Scholar Author, Article, and Disclosure InformationAuthors: Keith A. Marsolo, PhD; Kevin P. Weinfurt, PhD; Karen L. Staman, MS; Bradley G. Hammill, DrPHAffiliations: Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina (K.A.M., K.P.W., K.L.S., B.G.H.).Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or its HEAL Initiative.Financial Support: This work is supported within the NIH Health Care Systems Research Collaboratory by the NIH Common Fund through cooperative agreement U24AT009676 from the Office of Strategic Coordination within the Office of the NIH Director. This work is also supported by the NIH through the NIH HEAL Initiative under award U24AT010961.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M22-2973.Corresponding Author: Keith A. Marsolo, PhD, Population Health Sciences, Duke University, 300 West Morgan Street, Suite 636, Durham, NC 27701; e-mail, keith.marsolo@duke.edu.Author Contributions: Conception and design: B.G. Hammill, K.A. Marsolo, K.P. Weinfurt.Analysis and interpretation of the data: K.L. Staman.Drafting of the article: B.G. Hammill, K.A. Marsolo, K.L. Staman, K.P. Weinfurt.Critical revision for important intellectual content: B.G. Hammill, K.A. Marsolo, K.P. Weinfurt.Final approval of the article: B.G. Hammill, K.A. Marsolo, K.L. Staman, K.P. Weinfurt.Obtaining of funding: K.P. Weinfurt.This article was published at Annals.org on 31 January 2023. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics March 2023Volume 176, Issue 3Page: 402-403KeywordsAlgorithmsData managementDisclosureHealth careHealth Insurance Portability and Accountability ActReproducibilityResearch fundingScience policyStatistical dataStatistical methods ePublished: 31 January 2023 Issue Published: March 2023 Copyright & PermissionsCopyright © 2023 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,211 | 0,243 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,009 | 0,026 |
| Communication savante | 0,023 | 0,019 |
| Science ouverte | 0,004 | 0,018 |
| Intégrité de la recherche | 0,012 | 0,030 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».