Predictors of clinical trial data sharing: exploratory analysis of a cross-sectional survey
Notice bibliographique
Résumé
BACKGROUND: A number of research funders, biomedical journals, pharmaceutical companies, and regulatory agencies have adopted policies advocating or mandating that clinical trialists share data with external investigators. We therefore sought to determine whether certain characteristics of trialists or their trials are associated with more unfavorable perceptions of data sharing. To date, no prior research has addressed this issue. METHODS: We conducted an exploratory analysis of responses to a cross-sectional, web-based survey. The survey sample consisted of trialists who were corresponding authors of clinical trials published in 2010 or 2011 in one of six general medical journals with the highest impact factors in 2011. The following key characteristics were examined: trialists' academic productivity and geographic location, trial funding source and size, and the journal in which it was published. Main outcome measures included: support for data sharing in principle, concerns with data sharing through repositories, and reasons for granting or denying requests. Chi-squared tests and Fisher's exact tests were used to assess statistical significance. RESULTS: Of 683 potential respondents, 317 completed the survey (response rate 46%). Both support for data sharing and reporting of specific concerns with sharing data through repositories exceeded 75%, but neither differed by trialist or trial characteristics. However, there were some significant differences in explicit reasons to share or withhold data. Respondents located in Western Europe more frequently indicated they have or would share data in order to receive academic benefits or recognition when compared with respondents located in the United States or Canada (58 versus 31%). In addition, respondents who were the most academically productive less frequently indicated they have or would withhold data in order to protect research subjects when compared with less academically productive respondents (24 versus 40%), as did respondents who received industry funding when compared with those who had not (24 versus 43%). CONCLUSIONS: Respondents indicated strong support for data sharing overall. There were few notable differences in how trialists viewed the benefits and risks of data sharing when categorized by trialists' academic productivity and geographic location, trial funding source and size, and the journal in which it was published.
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 | 0,015 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».