Developing and Testing a Tool for the Classification of Study Designs in Systematic Reviews of Interventions and Exposures
Notice bibliographique
Résumé
Background Classification of study design can help provide a common language for researchers. Within a systematic review, definition of specific study designs can help guide inclusion, assess the risk of bias, pool studies, interpret results, and grade the body of evidence. However, recent research demonstrated poor reliability for an existing classification scheme. Objectives To review tools used to classify study designs; to select a tool for evaluation; to develop instructions for application of the tool to intervention/exposure studies; and to test the tool for accuracy and interrater reliability. Methods We contacted representatives from all AHRQ Evidence-based Practice Centers (EPCs), other relevant organizations, and experts in the field to identify tools used to classify study designs. Twenty-three tools were identified; 10 were relevant to our objectives. The Steering Committee ranked the 10 tools using predefined criteria. The highest-ranked tool was a design algorithm for studies of health care interventions developed, but no longer advocated, by the Cochrane Non-Randomised Studies Methods Group. This tool was used as the basis for our classification tool and was revised to encompass more study designs and to incorporate elements of other tools. A sample of 30 studies was used to test the tool. Three members of the Steering Committee developed a reference standard (i.e., the “true” classification for each study); 6 testers applied the revised tool to the studies. Interrater reliability was measured using Fleiss’ kappa (κ) and accuracy of the testers’ classification was assessed against the reference standard. Based on feedback from the testers and the reference standard committee, the tool was further revised and tested by another 6 testers using 15 studies randomly selected from the original sample. Results In the first round of testing the inter-rater reliability was fair among the testers (κ = 0.26) and the reference standard committee (κ = 0.33). Disagreements occurred at all decision points in the algorithm; revisions were made based on the feedback. The second round of testing showed improved interrater reliability (κ = 0.45, moderate agreement) with improved, but still low, accuracy. The most common disagreements were whether the study was “experimental” (5/15 studies) and whether there was a comparison (4/15 studies). In both rounds of testing, the level of agreement for testers who had completed graduate-level training was higher than for testers who had not completed training. Conclusion Potential reasons for the observed low reliability and accuracy include the lack of clarity and comprehensiveness of the tool, inadequate reporting of the studies, and variability in user characteristics. Application of a tool to classify study designs in the context of a systematic review should be accompanied by adequate training, pilot testing, and documented decision rules.
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 | 0,814 | 0,940 |
| Méta-épidémiologie (sens strict) | 0,010 | 0,009 |
| Méta-épidémiologie (sens large) | 0,016 | 0,033 |
| Bibliométrie | 0,076 | 0,053 |
| Études des sciences et des technologies | 0,009 | 0,013 |
| Communication savante | 0,023 | 0,029 |
| Science ouverte | 0,011 | 0,024 |
| Intégrité de la recherche | 0,013 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,003 |
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 ».