PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews
Notice bibliographique
Résumé
The methods and results of systematic reviews should be reported in sufficient detail to allow users to assess the trustworthiness and applicability of the review findings.The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement was developed to facilitate transparent and complete reporting of systematic reviews and has been updated (to PRISMA 2020) to reflect recent advances in systematic review methodology and terminology.Here, we present the explanation and elaboration paper for PRISMA 2020, where we explain why reporting of each item is recommended, present bullet points that detail the reporting recommendations, and present examples from published reviews.We hope that changes to the content and structure of PRISMA 2020 will facilitate uptake of the guideline and lead to more transparent, complete, and accurate reporting of systematic reviews.Systematic reviews are essential for healthcare providers, policy makers, and other decision makers, who would otherwise be confronted by an overwhelming volume of research on which to base their decisions.To allow decision makers to assess the trustworthiness and applicability of review findings, reports of systematic reviews should be transparent and complete.Furthermore, such reporting should allow others to replicate or update reviews.The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement published in 2009 (hereafter referred to as PRISMA 2009) 1-12 was designed to help authors prepare transparent accounts of their reviews, and its recommendations have been widely endorsed and adopted.13 We have updated the PRISMA 2009 statement (to PRISMA 2020) to ensure currency and relevance and to reflect advances in systematic review methodology and terminology. Scope of this guidelineThe PRISMA 2020 statement has been designed primarily for systematic reviews of studies that evaluate the effects of health interventions, irrespective of the design of the included studies.However, the checklist items are applicable to reports of systematic reviews evaluating other non-health-related interventions (for example, social or educational interventions), and many items are applicable to systematic reviews with objectives other than evaluating interventions (such as evaluating aetiology, prevalence, or prognosis).PRISMA 2020 is intended for use in systematic reviews that include synthesis (such as pairwise metaanalysis or other statistical synthesis methods) or do not include synthesis (for example, because only one eligible study is identified).The PRISMA 2020 items are relevant for mixed-methods systematic reviews (which include quantitative and qualitative studies), but reporting guidelines addressing the presentation and synthesis of qualitative data should also be consulted.14 15 PRISMA 2020 can be used for original systematic reviews, updated systematic reviews, or continually updated ("living") systematic reviews.However, for updated and living systematic reviews, there may be some additional considerations that need to be addressed.Extensions to the PRISMA 2009 statement have been developed to guide reporting of network meta-analyses, 16 meta-analyses of individual participant data, 17 systematic reviews of harms, 18 systematic reviews of diagnostic test accuracy studies, 19 and scoping reviews 20 ; for these types of reviews we recommend authors report their review For numbered affiliations see end of the article.
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 | 0,341 | 0,608 |
| Méta-épidémiologie (sens strict) | 0,008 | 0,016 |
| Méta-épidémiologie (sens large) | 0,013 | 0,033 |
| Bibliométrie | 0,022 | 0,031 |
| Études des sciences et des technologies | 0,003 | 0,007 |
| Communication savante | 0,013 | 0,011 |
| Science ouverte | 0,012 | 0,012 |
| Intégrité de la recherche | 0,014 | 0,033 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,093 | 0,062 |
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 ».