Risk of bias in exercise science: A systematic review of 340 studies
Bibliographic record
Abstract
Risk of bias can contribute to irreproducible science and mislead decision making. Analyses of smaller subsections of the exercise science literature suggest many exercise science studies have unclear or high risk of bias. The current review (osf.io/jznv8) assesses whether this unclear or high risk of bias is more widespread in the exercise science literature and whether this bias has decreased since the publication of the 1996 Consolidated Standards of Reporting Trials (CONSORT) guidelines. We report significant reductions in selection, performance, detection, and reporting biases in 2020 compared with 1995 in the 340 of 5,451 studies assessed using the Cochrane Risk of Bias tool. Despite these improvements, most 2020 studies still had unclear or high risks of bias. These results underscore the need for methodological vigilance, adherence to reporting standards, and education on experimental bias. Factors contributing to these improvements, such advancements in education and journal requirements, remain uncertain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.451 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.035 | 0.006 |
| Bibliometrics | 0.003 | 0.028 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.010 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".