The trustworthiness and impact of trial preprints for COVID-19 decision-making: A methodological study
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Notice bibliographique
Résumé
Purpose: To assess the trustworthiness and impact of preprint trial reports during the COVID-19 pandemic. Data sources: WHO COVID-19 database and the L-OVE COVID-19 platform by the Epistemonikos Foundation (up to August 3rd, 2021) Design: We compare the characteristics of COVID-19 trials with and without preprints, estimate time to publication of COVID-19 preprint reports, describe discrepancies in key methods and results between preprint and published trial reports, report the number of retracted preprints and publications, and assess whether including versus excluding preprint reports affects meta-analytic estimates and the certainty of evidence. For the effects of eight therapies on mortality and mechanical ventilation, we performed meta-analyses including preprints and excluding preprints at 1 month, 3 months, and 6 months after the first trial addressing the therapy became available either as a preprint or publication (120 meta-analyses in total). Results: We included 356 trials, 101 of which are only available as preprints, 181 as journal publications, and 74 as preprints first and subsequently published in journals. Half of all preprints remain unpublished at six months and a third at one year. There were few important differences in key methods and results between trial preprints and their subsequent published reports. We identified four retracted trials, three of which were published in peer-reviewed journals. With two exceptions (2/60; 3.3%), point estimates were consistent between meta-analyses including versus excluding preprints as to whether they indicated benefit, no appreciable effect, or harm. There were nine comparisons (9/60; 15%) for which the rating of the certainty of evidence differed when preprints were included versus excluded, for four of these comparisons the certainty of evidence including preprints was higher and for five of these comparisons the certainty of evidence including preprints was lower. Limitations: The generalizability of our results is limited to COVID-19. Preprints that are subsequently published in journals may be the most rigorous and may not represent all trial preprints. Conclusion: We found no compelling evidence that preprints provide less trustworthy results than published papers. We show that preprints remain the only source of findings of many trials for several months, for a length of time that is unacceptable in a health emergency. We show that including preprints may affect the results of meta-analyses and the certainty of evidence. We encourage evidence users to consider data from preprints in contexts in which decisions are being made rapidly and evidence is being produced faster than can be peer-reviewed.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,104 | 0,406 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,008 | 0,010 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 découle