Evidence-based decision-making: On the use of systematicity cases to check the compliance of reviews with reporting guidelines such as PRISMA 2020
Notice bibliographique
Résumé
Systematic reviews aim to provide high-quality evidence-based syntheses for efficacy under real-world conditions and allow understanding the correlations between exposures and outcomes. They are increasingly popular and have several stakeholders (e.g., healthcare providers, researchers, educators, students, journal editors, policy makers, managers) to whom they help make informed recommendations for practice or policy. Systematic reviews usually exhibit low methodological and reporting quality. To tackle this, reporting guidelines have been developed to support systematic reviews reporting and assessment. Following such guidelines is crucial to ensure that a review is transparent, complete, trustworthy, reproducible, and unbiased. However, systematic reviewers usually fail to adhere to existing reporting guidelines, which may significantly decrease the quality of the reviews they report and may result in systematic reviews that lack methodological rigor, yield low-credible findings and may mislead decision-makers. To assure that a review complies with reporting guidelines, we rely on assurance cases that are an emerging way of arguing and relaying various safety–critical systems’ requirements in an extensive manner, as well as checking the compliance of such systems with standards to support their certification. Since the nature of assurance cases makes them applicable to various domains and requirements/properties, we therefore propose a new type of assurance cases called systematicity cases. Systematicity cases focus on the systematicity property and allow arguing that a review is systematic i.e., that it sufficiently complies with the targeted reporting guideline. The most widespread reporting guidelines include PRISMA (Preferred Reporting Items for Systematic reviews and meta-Analyses). We measure the confidence in a systematicity case representing a review as a means to quantify the systematicity of that review i.e., the extent to which that review is systematic. We rely on rule-based Artificial Intelligence to create a knowledge-based system that automatically supports the inference mechanism that a given systematicity case embodies and that allows making a decision regarding the systematicity of a given review. An empirical evaluation performed on 25 reviews (self-identifying as systematic) showed that these reviews exhibit a suboptimal systematicity. More specifically, the systematicity of the analyzed reviews varies between 32.96% and 66.49% and its average is 54.42%. More efforts are therefore needed to report systematic reviews of higher quality. More experiments are also needed to further explore the factors hindering and/or assuring the systematicity of reviews. The main beneficiaries of our work are journal reviewers, journal editors, managers, policymakers, researchers, organizations developing reporting guidelines, peer reviewers, students, insurers, evidence users, as well as reporting guidelines developers.
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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 | 0,884 | 0,939 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,009 |
| Méta-épidémiologie (sens large) | 0,017 | 0,019 |
| Bibliométrie | 0,041 | 0,026 |
| Études des sciences et des technologies | 0,009 | 0,038 |
| Communication savante | 0,038 | 0,064 |
| Science ouverte | 0,019 | 0,034 |
| Intégrité de la recherche | 0,044 | 0,026 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,004 |
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 ».