Progression of Pilot Trials to Completed Randomized Controlled Trials in Plastic Surgery
Bibliographic record
Abstract
PURPOSE: Well-designed pilot trials are essential in determining feasibility prior to initiating definitive randomized controlled trials (RCTs) and their implementation into clinical practice. The primary outcome of this study was to identify the number of pilot or feasibility studies in Plastic Surgery that progressed to a definitive RCT. Secondary outcomes included a) number of pilot studies expressing feasibility statements and outcomes and b) reporting quality. METHODS: MEDLINE, Embase, Web of Science, and clinicaltrials.gov were searched for all pilot RCTs and definitive RCTs in plastic surgery between 2012-2023. Pilot trials were matched to definitive RCTs by keyword, author, and citation report. Feasibility outcomes were presented using descriptive statistics. Reporting quality was evaluated using the Consolidated Standards of Reporting Trials 2010 randomized pilot and feasibility trials extension. RESULTS: Among 11,540 and 6035 citations screened in 2 separate literature searches, 171 pilot studies and 779 definitive RCTS were included, respectively. Ten (5.8%) pilot studies were associated with a completed RCT, 4 (2.3%) were in progress, and 2 (1.2%) were stopped. For studies that did not progress to a definitive RCT, "inadequate funding" (n = 11, 41.4%) was the most cited reason followed by "insufficient efficacy to justify study progression" (n = 5, 17.3%). The average reporting adherence to the Consolidated Standards of Reporting Trials items was 65.6% (SD 16). Fifty (29.2%) pilot RCTs reported a feasibility statement and 30 (17.5%) reported feasibility outcomes. CONCLUSIONS: Few pilot trials in plastic surgery progressed to a definitive RCT, and most did not present feasibility statements or outcomes. Pilot studies should precede RCTs and include clear feasibility statements and outcomes.
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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 | 0.696 | 0.839 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".