Assessment of Risk of Bias in Osteosarcoma and Ewing’s Sarcoma Randomized Controlled Trials: A Systematic Review
Bibliographic record
Abstract
Aim: The aim of this study was to systematically assess the risk of bias in osteosarcoma and Ewing’s sarcoma (ES) randomized controlled trials (RCT) and to examine the relationships between bias and conflict of interest/industry sponsorship. Methods: An OVID-MEDLINE search was performed (1976–2019). Using the Cochrane Collaboration guidelines, two reviewers independently assessed the prevalence of risk of bias in different RCT design domains. The relationship between conflicts of interest and industry funding with the frequency of bias was examined. Results: 73 RCTs met inclusion criteria. Prevalence of low-risk bias domains was 47.3%, unclear-risk domains 47.8%, and 4.9% of the domains had a high-risk of bias. Domains with the highest risk of bias were blinding of participants/personnel and outcome assessors, followed by randomization and allocation concealment. Overtime, frequency of unclear-risk of bias domains decreased (χ2 = 5.32, p = 0.02), whilst low and high-risk domains increased (χ2 = 8.13, p = 0.004). Studies with conflicts of interest and industry sponsorships were 4.2 and 3.1 times more likely to have design domains with a high-risk of bias (p < 0.05). Conclusion: This study demonstrates that sources of potential bias are prevalent in both osteosarcoma and ES RCTs. Studies with financial conflicts of interest and industry sponsors were significantly more likely to have domains with a high-risk of bias. Improvements in reporting and adherence to proper methodology will reduce the risk of bias and improve the validity of the results of RCTs in osteosarcoma and ES.
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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 | 0.215 | 0.525 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.022 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".