Critical appraisal tools and rater training in systematic reviews and meta‐analyses
Notice bibliographique
Résumé
EDITOR—We appreciate the opportunity to respond to the letter by Rehm and Thahir1 on our systematic review and meta-analysis.2 First, the search strategy, developed by the researchers and supported by an information specialist, is in accordance with systematic review methodology and was transparently reported. The team created extensive and multiple terms – found via different approaches – to reduce bias, and truncation operators were used to increase sensitivity. Presently, there is no criterion standard tool for assessing the methodological quality in non-randomized studies for systematic reviews. Although the Newcastle–Ottawa Scale (NOS) is widely used and recommended by the Cochrane Collaboration, its lower interrater reliability has been recognized.3, 4 Nevertheless, Oremus et al.4 concluded that standardized rater training before assessment may improve agreement. In our study, three trained independent reviewers assessed the quality after piloting the scale. We fully agree that including randomized controlled trials (RCTs) would have lowered meta-analysis’ bias. In cases in which RCTs are not ethical nor possible, as with the topic of our research, case–control studies may be the best alternative. We agree that the selected controls in the case–control studies should be representative for their population. Therefore, we assigned a low NOS score for selection and definition of controls, see Table S5 in Van der Looven et al.2 Furthermore, as mentioned in detail in our limitation section,2 we acknowledge the presence of source bias of the included studies. Gutman et al. suggest that a time-dependent bias and selection bias, among other factors, could be responsible for the differences in diagnoses between different databases.5 However, given that the diagnosis of neonatal brachial plexus palsy (NBPP) is clinically obvious directly after birth, our meta-analysis is not likely to have been affected by this time-dependent factor. The Kids Inpatient Database is not subject to selection bias by a clustering effect. Moreover, indexed with the International Classification of Disease codes, it enables database comparison. We agree that the location and time distribution of the studies in our review is quite wide. However, the intent of our systematic review was to combine all available worldwide data from a predefined timespan to clarify our research question. Additionally, we feel the critical appraisal by Rehm and Thahir1 of our sensitivity analyses is quite flawed. The analyses were performed post hoc to clarify, not to decrease, the heterogeneity. The authors did not remove studies from the meta-analysis nor manipulate eligibility criteria. Rehm and Thahir1 also focus strongly on their doubts about the validity of the NBPP incidence decrease. Again, in our limitation section we clearly state that the additional incidence assessment is prone to selection and source bias as it was not the primary aim of the meta-analysis.2 Weighing of the different study data, including the Swedish registry data, was correctly applied to minimize bias for the incidence calculation. Overlapping study populations were systematically identified and only included once, as appropriate. We agree critical appraisal is an essential element of improving the reporting of data, therefore we appreciate the concerns of Rehm and Thahir.1 However, meta-analyses are complex studies that include a series of decisions to be made which can create room for subjectivity and, with that, critique. Therefore, a well-documented reporting practice, as recommended by the PRISMA statement, was transparently presented.
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 | 0,359 | 0,772 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,004 |
| Méta-épidémiologie (sens large) | 0,012 | 0,008 |
| Bibliométrie | 0,014 | 0,011 |
| Études des sciences et des technologies | 0,003 | 0,008 |
| Communication savante | 0,014 | 0,012 |
| Science ouverte | 0,016 | 0,006 |
| Intégrité de la recherche | 0,020 | 0,026 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,011 |
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 ».