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Record W7115678221 · doi:10.48448/x6fs-ra22

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

2025· other· W7115678221 on OpenAlexaboutno aff

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsObservational studyPsychological interventionCausal inferenceImpact factorSpecialtyLogistic regressionCohort studyMEDLINECitation

Abstract

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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.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.014
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.177
GPT teacher head0.422
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
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