Research waste from poor reporting of core methods and results and redundancy in studies of reporting guideline adherence: a meta-research review
Bibliographic record
Abstract
ABSTRACT Objectives We investigated meta-research studies that evaluated adherence to prominent reporting guidelines (CONSORT, PRISMA, STARD, STROBE) in health research studies to determine the proportion that (1) provided an explanation for how complex guideline items were rated for adherence and (2) provided results from individual studies reviewed in addition to aggregate results. We also examined the conclusions of each meta-research study to assess redundancy of findings across studies. Design Cross-sectional meta-research review. Data sources MEDLINE (Ovid) searched on July 5, 2022. Eligibility criteria for selecting studies Studies in any language were eligible if they used any version of the CONSORT, PRISMA, STARD, or STROBE reporting guidelines or their extensions to evaluate reporting in at least 10 human health research studies. We excluded studies that modified a reporting guideline or its items or evaluated fewer than half of reporting guideline items. Main outcomes were (1) the proportion of meta-research studies that provided a coding explanation that could be used to replicate the study or verify its results and (2) the proportion that provided individual-level study results in the main text, supplemental materials, or via an internet link. Results Of 148 included meta-research studies, 14 (10%, 95% confidence interval [CI] 6% to 15%) provided a fully replicable coding explanation, and 49 (33%, 95% CI 26% to 41%) completely reported individual study results. Of 90 studies that classified reporting as adequate or inadequate in the study abstract, 6 (7%, 95% CI 3% to 14%) concluded that reporting was adequate but none of those 6 studies provided information on how items were coded or provided item-level results for included studies. Conclusions Much of published meta-research on reporting in health research is likely wasteful. Few studies report enough information for verification or replication, and almost all find that reporting in health research studies is suboptimal. These findings highlight the importance of shifting the focus from assessing reporting adequacy to developing, testing, and implementing strategies to improve reporting. Funding There was no specific funding for this study. Protocol Posted on the Open Science Framework June 29, 2022 ( https://osf.io/gtm4z/ ).
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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 | 0.584 | 0.790 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".