209: UNLOCKING EVIDENCE REVERSAL IN THE LITERATURE: A KEY TO TERMINOLOGY
Notice bibliographique
Résumé
Background and aims: Evidence Reversal (ER) is the phenomenon whereby new evidence – most often strong randomized controlled trials – finds an already established clinical practice to be less effective, or even more harmful, than was originally believed. This phenomenon is very prevalent with up to 46% of trials testing an already established practice leading to a reversal of that practice. Before reducing the adverse impact of reversals on clinical practice, we must first understand the phenomenon and how it has been explored. The objectives of this review are to explore the terminology and definitions for ER in the literature and then map the terms onto a framework. Methods: Multiple academic and grey literature databases were systematically searched between 2000 and 2016 using combinations of relevant subject headings and key words. Hand searches of relevant journals, websites, and blogs were also performed. Two reviewers independently screened the returned citations and performed data extraction and quality assessment using a modified AMSTAR rating tool. All reviews and collections of studies that discussed aspects or examples of ER – either directly or indirectly – were included. Results: After the removal of duplicate citations, 48936 items were retrieved for screening. The final number of included reviews was 87. The concept of reversal first appeared in the literature in the early 2000s, but the majority of articles have been published in the past four years. Terms for ER that we found in our search include: medical reversal, de-implementation, de-adoption, un-diffusion, disinvestment, abandonment, discontinuation, Proteus phenomenon, contradicted findings, POEMS likely to change practice, evidence to change practice, and overtreatment. These terms, and others, have been mapped onto a framework for identifying reversal in the literature. The overall quality of the articles was very low. Conclusion: Evidence reversal, though not a new phenomenon, has only recently been explored in the literature. There are many different terms for the process of reversal and identifying medical practices to be targeted for reversal. Consensus should be reached on which terms are most appropriate so that subject headings can be developed and cohesion can be brought to this emerging field of meta-research.
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 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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».