Meta-analytic comparison of randomized and nonrandomized studies of breast cancer surgery
Bibliographic record
Abstract
BACKGROUND: Randomized controlled trials (RCTs) are thought to provide the most accurate estimation of "true" treatment effect. The relative quality of effect estimates derived from nonrandomized studies (nRCTs) remains unclear, particularly in surgery, where the obstacles to performing high-quality RCTs are compounded. We performed a meta-analysis of effect estimates of RCTs comparing surgical procedures for breast cancer relative to those of corresponding nRCTs. METHODS: English-language RCTs of breast cancer treatment in human patients published from 2003 to 2008 were identified in MEDLINE, EMBASE and Cochrane databases. We identified nRCTs using the National Library of Medicine's "related articles" function and reference lists. Two reviewers conducted all steps of study selection. We included studies comparing 2 surgical arms for the treatment of breast cancer. Information on treatment efficacy estimates, expressed as relative risk (RR) for outcomes of interest in both the RCTs and nRCTs was extracted. RESULTS: We identified 12 RCTs representing 10 topic/outcome combinations with comparable nRCTs. On visual inspection, 4 of 10 outcomes showed substantial differences in summary RR. The pooled RR estimates for RCTs versus nRCTs differed more than 2-fold in 2 of 10 outcomes and failed to demonstrate consistency of statistical differences in 3 of 10 cases. A statistically significant difference, as assessed by the z score, was not detected for any of the outcomes. CONCLUSION: Randomized controlled trials comparing surgical procedures for breast cancer may demonstrate clinically relevant differences in effect estimates in 20%-40% of cases relative to those generated by nRCTs, depending on which metric is used.
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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 | 0.140 | 0.362 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.059 |
| Bibliometrics | 0.023 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".