A Systematic Review of Surgical Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: The authors investigated the methodological validity of plastic surgery randomized controlled trials that compared surgical interventions. METHODS: An electronic search identified randomized controlled trials published between 2000 and 2013. Reviewers, independently and in duplicate, assessed manuscripts and performed data extraction. Methodological safeguards (randomization, allocation concealment, blinding, and incomplete outcome data) were examined using the Cochrane risk of bias tool. Regression analysis was used to identify trial characteristics associated with risk of bias. RESULTS: Of 1664 potentially eligible studies, 173 randomized controlled trials were included. Proper randomization and allocation concealment methods were described in 61 of 173 (35 percent) and 21 of 173 (12 percent), respectively. Outcome assessors were blinded in 58 of 173 (34 percent) trials, and patients were blinded in 45 of 173 (26 percent). Follow-up rates were high, with 99 of 173 (57 percent) randomized controlled trials appearing to have complete follow-up. An intention-to-treat analysis was used in 19 of 173 (11 percent) trials. One-third (58 of 173, 34 percent) did not state their primary outcomes. The most common type of primary outcome used was a symptom/quality of life, class III, outcome (73 of 173, 42 percent). Multinomial regression demonstrated trials reporting an a priori sample size as more likely to have a low risk of bias (p = 0.001). CONCLUSIONS: This article highlights methodological safeguards that plastic surgeons should consider when interpreting results of a surgical randomized controlled trial. Allocation concealment, outcome assessor blinding, and patient blinding were identified as areas of concern. Valid and reliable outcome measures are being used in plastic surgery. This analysis provides strong rationale for continued focus on the performance and reporting of clinical trials within our specialty.
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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 | 0.736 | 0.942 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.354 | 0.124 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".