Reporting quality and risk of bias in randomised trials in health professions education
Bibliographic record
Abstract
CONTEXT: Complete reporting of research is essential to enable consumers to accurately appraise, interpret and apply findings. Quality appraisal checklists are giving way to tools that judge the risk for bias. OBJECTIVES: We sought to determine the prevalence of these complementary aspects of research reports (completeness of reporting and perceived risk for bias) of randomised studies in health professions education. METHODS: We searched bibliographic databases for randomised studies of health professions education. We appraised two cohorts representing different time periods (2008-2010 and 2014, respectively) and worked in duplicate to apply the CONSORT guidelines and Cochrane Risk of Bias tool. We explored differences between time periods using independent-samples t-tests or the chi-squared test, as appropriate. RESULTS: We systematically identified 180 randomised studies (2008-2010, n = 150; 2014, n = 30). Frequencies of reporting of CONSORT elements within full-text reports were highly variable and most elements were reported in fewer than 50% of studies. We found a statistically significant difference in the CONSORT reporting index (maximum score: 500) between the 2008-2010 (mean ± standard deviation [SD]: 242.7 ± 55.6) and 2014 (mean ± SD: 311.6 ± 53.2) cohorts (p < 0.001). High or unclear risk for bias was most common for allocation concealment (157, 87%) and blinding of participants (147, 82%), personnel (152, 84%) and outcome assessors (112, 62%). Most risk for bias elements were judged to be unclear (range: 51-84%). Risk for bias elements significantly improved over time for blinding of participants (p = 0.007), incomplete data (p < 0.001) and the presence of other sources of bias (p < 0.001). CONCLUSIONS: Reports of randomised studies in health professions education frequently omit elements recommended by the CONSORT statement. Most reports were assessed as having a high or unclear risk for bias. Greater attention to how studies are reported at study outset and in manuscript preparation could improve levels of complete transparent reporting.
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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 | 0.734 | 0.885 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.016 |
| Bibliometrics | 0.029 | 0.031 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".