Scoping review of registration of observational studies finds inadequate registration policies, increased registration, and a debate converging toward proregistration
Bibliographic record
Abstract
OBJECTIVES: We aimed to examine a) the policies of national and international clinical trial registries regarding observational studies; b) the time trends of observational study registration; and c) the published arguments for and against observational study registration. STUDY DESIGN AND SETTING: Scoping review of registry practices and published arguments. We searched the websites and databases of all 19 members of the World Health Organization's Registry Network to identify policies relating to observational studies and the number of observational studies registered annually from the beginning of the registries to 2022. Regarding documents with arguments, we searched Medline, Embase, Google Scholar, and top medical and epidemiological journals from 2009 to 2023. We classified arguments as "main" based on the number (n ≥ 3) of documents they occurred in. RESULTS: Of 19 registries, 15 allowed observational study registration, of which seven (35%) had an explicit policy regarding what to register and two (11%) about when to register. The annual number of observational study registrations increased over time in all registries; for example, ClinicalTrials.gov increased from 313 in 1999 to 9775 in 2022. Fifty documents provided arguments concerning observational study registration: 31 argued for, 18 against, and one was neutral. Since 2012, 19 out of 25 documents argued for. We classified nine arguments as main: five for and four against. The two most prevalent arguments for were the prevention of selective reporting of outcomes (n = 16) and publication bias (n = 12), and against were that it will hinder exploration of new ideas (n = 17) and it will waste resources (n = 6). CONCLUSION: Few registries have policies regarding observational studies; an increasing number of observational studies were registered; there was a lively debate on the merits of registration of observational studies, which, since 2012, seems to converge toward proregistration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.724 | 0.879 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.043 | 0.052 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".