Completely Autologous Platelet Gel in Breast Reduction Surgery: A Blinded, Randomized, Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to assess the effectiveness of topical application of completely autologous platelet gel during breast surgery to reduce postoperative wound drainage. An increasing number of surgical centers are using tissue sealants to reduce postoperative drainage and improve surgical outcomes. However, there is a paucity of randomized, double-blind, controlled trials assessing the efficacy of these agents. METHODS: The authors conducted a within-patient, randomized, patient- and assessor-blinded, controlled trial assessing the use of completely autologous platelet gel in 111 patients undergoing bilateral reduction mammaplasty. Patients were randomized to receive the gel applied to the left or right breast after hemostasis was achieved; the other breast received no treatment. The primary outcome was the difference in wound drainage over 24 hours. Secondary outcomes included subjective and objective assessments of pain and wound healing. RESULTS: No statistically significant differences in the drainage, level of pain, size of open areas, clinical appearance, degree of scar pliability, or scar erythema were noted. CONCLUSION: The authors' results do not support the use of completely autologous platelet gel to improve outcomes after reduction mammaplasty.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".