State of reporting of primary biomedical research: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Incomplete or inconsistent reporting remains a major concern in the biomedical literature. Incomplete or inconsistent reporting may yield the published findings unreliable, irreproducible or sometimes misleading. In this study based on evidence from systematic reviews and surveys that have evaluated the reporting issues in primary biomedical studies, we aim to conduct a scoping review with focuses on (1) the state-of-the-art extent of adherence to the emerging reporting guidelines in primary biomedical research, (2) the inconsistency between protocols or registrations and full reports and (3) the disagreement between abstracts and full-text articles. METHODS AND ANALYSES: We will use a comprehensive search strategy to retrieve all available and eligible systematic reviews and surveys in the literature. We will search the following electronic databases: Web of Science, Excerpta Medica Database (EMBASE), MEDLINE and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Our outcomes are levels of adherence to reporting guidelines, levels of consistency between protocols or registrations and full reports and the agreement between abstracts and full reports, all of which will be expressed as percentages, quality scores or categorised rating (such as high, medium and low). No pooled analyses will be performed quantitatively given the heterogeneity of the included systematic reviews and surveys. Likewise, factors associated with improved completeness and consistency of reporting will be summarised qualitatively. The quality of the included systematic reviews will be evaluated using AMSTAR (a measurement tool to assess systematic reviews). ETHICS AND DISSEMINATION: All findings will be published in peer-reviewed journals and relevant conferences. These results may advance our understanding of the extent of incomplete and inconsistent reporting, factors related to improved completeness and consistency of reporting and potential recommendations for various stakeholders in the biomedical community.
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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 | 0.431 | 0.476 |
| Meta-epidemiology (narrow) | 0.007 | 0.009 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.027 | 0.030 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.082 | 0.041 |
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".