Reporting and data sharing level for COVID-19 vaccine trials: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The results and data availability of vaccine trials directly affect the decisions of healthcare providers, the public, and policymakers as to whether the vaccine should be applied. However, the reporting and data sharing level of COVID-19 vaccine studies are not clear. METHODS: A cross-sectional study was conducted. A systematic search up to 9 May 2021 in 12 databases and an updated search to 6 July 2021 were conducted in the Cochrane Living Systematic Review and Network Meta-Analysis database to identify COVID-19 vaccine trials. The basic characteristics of included trials were summarized. The reporting level was assessed according to the CONSORT checklist. The data sharing level was assessed by open science practices. Types of incomplete reporting including protocol deviation, lack of primary outcomes clarity, and the omission of harms were analyzed. FINDINGS: Finally, thirty-six COVID-19 vaccine articles reporting on 40 randomized controlled trials were included in this analysis. Based on the CONSORT checklist, the mean reporting score was 29.7 [95% confidence interval 28.7, 30.7]. Thirty-one articles (31/36, 86.1%) had data sharing statements, twenty-five articles (25/36, 69.4%) provided access to the source data. Twenty-seven articles (27/36, 75.0%) had protocol deviation, lack of primary outcomes clarity, or the omission of harms. INTERPRETATION: The reporting and data sharing level of COVID-19 vaccine trials were not optimal. We hope that the reporting and data sharing of future trials will be improved. We recommend establishing a comprehensive, accurate data sharing system for future vaccine trials. FUNDING: This work was supported by the National Key R&D Program of China (2019YFC1710400; 2019YFC1710403).
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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 | 0.594 | 0.768 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".