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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,<sup>1,2</sup> Zexing Song,<sup>3</sup> An-Wen Chan,<sup>3,4</sup> Asbjørn Hróbjartsson<sup>1,2</sup> <h4>Objective </h4> Observational studies of interventions use causal inference to assess the impact of interventions on health-related outcomes.<sup>1</sup> Despite concerns about reporting bias, observational studies are not subject to the same registration requirements as clinical trials.<sup>2,3</sup> 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. <h4>Design </h4> 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. <h4>Results </h4> 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 (&lt;1 month of their start date) (<b>Table 25-1069</b>). 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]; <i>P</i> = .002; no public protocol vs public protocol: OR, 0.04 [95% CI, 0.01-0.23]; <i>P</i> &lt; .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 <h4>Conclusions </h4> 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. <h4>References</h4> 1. Hernán MA, Wang W, Leaf DE. Target trial emulation: a framework for causal inference from observational data. <i>JAMA</i>. 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? <i>CMAJ</i>. 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. <i>J Clin Epidemiol</i><span lang="da-DK">. 2024;170:111341. doi:10.1016/j.jclinepi.2024.111341</span> <sup>1</sup>Cochrane Denmark &amp; Centre for Evidence-Based Medicine Odense (CEBMO), University of Southern Denmark, Odense, Denmark, ceciliejespersen@health.sdu.dk; <sup>2</sup>Open Patient data Explorative Network (OPEN), Odense University Hospital, Odense, Denmark; <sup>3</sup>Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; <sup>4</sup>Women’s College Research Institute, Dept. of Medicine, University of Toronto, Toronto, Ontario, Canada. <h4>Conflict of Interest Disclosures</h4> 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. <h4>Acknowledgments</h4> 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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,011
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,598
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,011
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0030,002
Études des sciences et des technologies0,0010,014
Communication savante0,0000,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
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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