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Enregistrement W7115678221 · doi:10.48448/x6fs-ra22

Registration of Observational Studies of Interventions: Prevalence, Characteristics, and Journal Policies

2025· other· W7115678221 sur OpenAlexaboutno aff

Notice bibliographique

RevueUnderline Science Inc. · 2025
Typeother
Langue
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésObservational studyPsychological interventionCausal inferenceImpact factorSpecialtyLogistic regressionCohort studyMEDLINECitation

Résumé

récupéré en direct d'OpenAlex

Cecilie Jespersen,1,2 Zexing Song,3 An-Wen Chan,3,4 Asbjørn Hróbjartsson1,2 Objective Observational studies of interventions use causal inference to assess the impact of interventions on health-related outcomes.1 Despite concerns about reporting bias, observational studies are not subject to the same registration requirements as clinical trials.2,3 We aimed to determine the prevalence of registration among published observational studies of interventions, assess the association between registration and study characteristics, analyze journal registration policies, and explore authors’ and editors’ attitudes about registration. Design We conducted a meta-epidemiologic cross-sectional study triangulating data from 4 sources. First, we searched PubMed for observational studies published in 2023. Eligible studies were cohort or case-control studies with a control group that assessed causal effects of health interventions. Corresponding registration information was collected. Second, authors of included studies were surveyed to explore reasons for and barriers to registration. Third, editorial policies were sampled from 40 journals: 20 sample-representative journals and the journals ranked in the top 20 in Journal Citation Reports by 2023 Journal Impact Factor across 8 specialty categories. Fourth, 1 editor per journal was invited to share their perspectives on registration. Primary outcomes were the prevalence of registered observational studies of interventions published in 2023 and the estimated association between registration and study characteristics, assessed by multivariable logistic regression. Sample size was estimated based on an expected 15% registration rate. Results Among 1100 screened studies, 200 were included: 69 and 128 cohort studies with prospective and retrospective data collection, respectively, and 3 case-control studies. In total, 28 (14%) were registered, and 17 of these (61%) were prospectively registered (<1 month of their start date) (Table 25-1069). Prospective design and protocol availability were positively associated with registration (retrospective vs prospective cohort: odds ratio [OR], 0.19 [95% CI, 0.07-0.54]; P = .002; no public protocol vs public protocol: OR, 0.04 [95% CI, 0.01-0.23]; P < .001). The survey response rate was 23% (46 responses); 60% of authors supported registration, although many only when registration was deemed relevant. Identified barriers included lack of journal requirements for registration (56%) and limited resources (62%). None of the journal policies explicitly required registration of observational studies of interventions, while 12 (30%) encouraged it. Journals that encouraged registration had a higher 2023 Journal Impact Factor and more frequently encouraged public protocols. Editors had divergent opinions on registration. While some considered it to be worthwhile, just as many questioned the added value. https://assets.underline.io/markdown_image/1/image/99ddb5f59ea342d2117443673974b4db.png Conclusions Only 1 in 7 contemporary observational studies of interventions were registered, although more often in cohort studies with prospective data collection and studies with a publicly available protocol. Authors identified the lack of journal requirements to registration as a key registration barrier, and only one-third of journals had supportive policies. Clearer guidance and journal policies on registration relevance (discriminating hypothesis-testing and hypothesis-generating studies) may reduce the risk of reporting biases in observational studies of interventions. References 1. Hernán MA, Wang W, Leaf DE. Target trial emulation: a framework for causal inference from observational data. JAMA. 2022;328(24):2446-2447. doi:10.1001/jama.2022.21383 2. Williams RJ, Tse T, Harlan WR, Zarin DA. Registration of observational studies: is it time? CMAJ. 2010;182(15):1638-1642. doi:10.1503/cmaj.09225 3. Leducq S, Zaki F, Hollestein LM, et al. The majority of observational studies in leading peer-reviewed medicine journals are not registered and do not have a publicly accessible protocol: a scoping review. J Clin Epidemiol. 2024;170:111341. doi:10.1016/j.jclinepi.2024.111341 1Cochrane Denmark & Centre for Evidence-Based Medicine Odense (CEBMO), University of Southern Denmark, Odense, Denmark, ceciliejespersen@health.sdu.dk; 2Open Patient data Explorative Network (OPEN), Odense University Hospital, Odense, Denmark; 3Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; 4Women’s College Research Institute, Dept. of Medicine, University of Toronto, Toronto, Ontario, Canada. Conflict of Interest Disclosures An-Wen Chan is a member of the Peer Review Congress Advisory Board but was not involved in the review or decision for this abstract. No other disclosures were reported. Acknowledgments We thank all researchers who participated in the author survey for their valuable contribution to the findings of this study.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,380
score de la tête « metaresearch » (Gemma)0,761
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Présentation des résultats · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,620
Score d'incertitude au seuil0,764

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,3800,761
Méta-épidémiologie (sens strict)0,0010,002
Méta-épidémiologie (sens large)0,0020,003
Bibliométrie0,0180,031
Études des sciences et des technologies0,0030,005
Communication savante0,0070,007
Science ouverte0,0030,005
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0080,002

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.

Tête enseignante Opus0,177
Tête enseignante GPT0,422
Écart entre enseignants0,245 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
DomainePrésentation des résultats
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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