Plastic Surgery Clinical Trials: A Systematic Review of Characteristics, Research Themes, and Predictors of Publication and Discontinuation
Bibliographic record
Abstract
Summary: Clinical trials (CTs) are crucial for evidence-based surgical care. Despite growing interest in plastic and reconstructive surgery (PRS) research, the status of PRS CTs remains unknown. We obtained PRS CTs from ClinicalTrials.gov and WHO’s International Clinical Trials Registry Platform (November 2022). Topic modeling identified research themes and machine learning models generated CT-publication pairs. Kaplan-Meier curves visualized CT discontinuation and nonpublication. Of the 4685 PRS CTs identified, 79% were interventional and 81% recruited adults. Most were single-center-led (67%) and academic-funded (77%). Male investigators led 77% of CTs. Female-only patient CTs outnumbered male-only (31% versus 1%). The United States led with 41% of CTs, followed by France, Canada, and China. Industry-funded CTs were higher in the USA, Germany, and Belgium, and academic-funded in France, Canada, and China. PRS CTs clustered into aesthetics (43%), reconstructive (20%), wound healing (8%), peripheral nerve (6%), tumor excision (5%), craniofacial (5%), perioperative pain (5%), and burns (4%). Industry preferred funding aesthetics, whereas academia and industry co-funded wound healing. Publication rates of completed (24%) and terminated (10%) CTs varied by cluster, with perioperative pain CTs exhibiting higher rates. Industry-funded CTs had lower publication rates (hazard ratio: 0.64, 95% confidence interval: 0.5–0.81) and higher discontinuation (hazard ratio: 1.34, confidence interval: 1.06–1.68) driven by sponsors’ decision to terminate prematurely. Global growth in PRS trialome reflects rising interest in evidence-based plastic surgery. Yet, imbalances in participant age, geography, funding source, and trial design influence likelihood of CT discontinuation and publication. Key research gaps include pediatric CTs, accountability in industry-funded research, and multicenter collaborations with underrepresented regions.
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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 | 0.151 | 0.452 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.022 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".