Risk of bias assessment of randomised controlled trials in high-impact ophthalmology journals and general medical journals: a systematic review
Bibliographic record
Abstract
Evidence-based treatments in ophthalmology are often based on the results of randomised controlled trials. Biased conclusions from randomised controlled trials may lead to inappropriate management recommendations. This systematic review investigates the prevalence of bias risk in randomised controlled trials published in high-impact ophthalmology journals and ophthalmology trials from general medical journals. Using Ovid MEDLINE, randomised controlled trials in the top 10 high-impact ophthalmology journals in 2015 were systematically identified and critically appraised for the prevalence of bias risk. Included randomised controlled trials were assessed in all domains of bias as defined by the Cochrane Collaboration. In addition, the prevalence of conflict of interest and industry sponsorship was investigated. A comparison with ophthalmology articles from high-impact general medical journals was performed. Of the 259 records that were screened from ophthalmology-specific journals, 119 trials met all inclusion criteria and were critically appraised. In total, 29.4% of domains had an unclear risk, 13.8% had a high risk and 56.8% had a low risk of bias. In comparison, ophthalmology articles from general medical journals had a lower prevalence of unclear risk (17.1%), higher prevalence of high risk (21.9%) and a higher prevalence of low risk domains (61.9%). Furthermore, 64.7% of critically appraised trials from ophthalmology-specific journals did not report any conflicts of interest, while 70.6% did not report an industry sponsor of their trial. In closing, it is essential that authors, peer reviewers and readers closely follow published risk of bias guidelines.
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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 | 0.338 | 0.737 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.030 | 0.025 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".