Changing patterns in reporting and sharing of review data in systematic reviews with meta-analysis of the effects of interventions: a meta-research study
Bibliographic record
Abstract
Objectives: To examine changes in completeness of reporting and frequency of sharing data, analytic code and other review materials in systematic reviews (SRs) over time; and factors associated with these changes. Design: Cross-sectional meta-research study. Sample: A random sample of 300 SRs with meta-analysis of aggregate data on the effects of a health, social, behavioural or educational intervention, which were indexed in PubMed, Science Citation Index, Social Sciences Citation Index, Scopus and Education Collection in November 2020. Analysis/Outcomes: The extent of complete reporting and frequency of sharing review materials in these reviews were compared with 110 SRs indexed in February 2014. Associations between completeness of reporting and various factors (e.g. self-reported use of reporting guidelines, journal's data sharing policies) were examined by calculating risk ratios (RR) and 95% confidence intervals (CI). Results: Several items were reported sub-optimally among 300 SRs from 2020, such as a registration record for the review (38%), a full search strategy for at least one database (71%), methods used to assess risk of bias (62%), methods used to prepare data for meta-analysis (34%), and funding source for the review (72%). Only a few items not already reported at a high frequency in 2014 were reported more frequently in 2020. There was no evidence that reviews using a reporting guideline were more completely reported than reviews not using a guideline. Reviews published in 2020 in journals that mandated either data sharing or inclusion of Data Availability Statements were more likely to share their review materials (e.g. data, code files) (18% vs 2%). Conclusion: Incomplete reporting of several recommended items for systematic reviews persists, even in reviews that claim to have followed a reporting guideline. Data sharing policies of journals potentially encourage sharing of review materials.
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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 | 0.622 | 0.845 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.032 |
| Bibliometrics | 0.025 | 0.044 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".