Risk of bias assessment tools often addressed items not related to risk of bias and used numerical scores
Notice bibliographique
Résumé
OBJECTIVES: We aimed to determine whether the existing risk of bias assessment tools addressed constructs other than risk of bias or internal validity and whether they used numerical scores to express quality, which is discouraged and may be a misleading approach. METHODS: We searched Ovid MEDLINE and Embase to identify quality appraisal tools across all disciplines in human health research. Tools designed specifically to evaluate reporting quality were excluded. Potentially eligible tools were screened by independent pairs of reviewers. We categorized tools according to conceptual constructs and evaluated their scoring methods. RESULTS: We included 230 tools published from 1995 to 2023. Access to the tool was limited to a peer-reviewed journal article in 63% of the sample. Most tools (76%) provided signaling questions, whereas 39% produced an overall judgment across multiple domains. Most tools (93%) addressed concepts other than risk of bias, such as the appropriateness of statistical analysis (65%), reporting quality (64%), indirectness (41%), imprecision (38%), and ethical considerations and funding (22%). Numerical scoring was used in 25% of tools. CONCLUSION: Currently available study quality assessment tools were not explicit about the constructs addressed by their items or signaling questions and addressed multiple constructs in addition to risk of bias. Many tools used numerical scoring systems, which can be misleading. Limitations of the existing tools make the process of rating the certainty of evidence more difficult. PLAIN LANGUAGE SUMMARY: Many tools have been made to assess how well a scientific study was designed, conducted, and written. We searched for these tools to better understand the types of questions they ask and the types of studies to which they apply. We found 230 tools published between 1995 and 2023. One in every four tools used a numerical scoring system. This approach is not recommended because it does not distinguish well between different ways quality can be assessed. Tools assessed quality in a number of different ways, with the most common ways being risk of bias (how a study is designed and run to reduce biased results; 98%), statistical analysis (how the data were analyzed; 65%), and reporting quality (whether important details were included in the article; 64%). People who make tools in the future should carefully consider the aspects of quality that they want the tool to address and distinguish between questions of study design, conduct, analysis, ethics, and reporting.
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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 | 0,661 | 0,885 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,014 | 0,013 |
| Bibliométrie | 0,043 | 0,041 |
| Études des sciences et des technologies | 0,004 | 0,010 |
| Communication savante | 0,014 | 0,020 |
| Science ouverte | 0,008 | 0,010 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».