Prevention of Hematoma in Patients Undergoing Facelift (Rhytidectomy): A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Hematoma is a known complication of rhytidectomy, and improved understanding of its incidence and the impact of adjunctive measures is essential to guide evidence-based practice. OBJECTIVE: Compare hematoma rates among patients undergoing deep plane facelift in those where either tranexamic acid (TXA), hemostatic nets, or tissue sealants are used. METHODS: A review was conducted in Ovid MEDLINE, EMBASE, PubMed MEDLINE, Cochrane, and SCOPUS to identify studies that employed deep plane facelifts and reported hematomas. The primary outcome was hematoma rates. Secondary endpoints included rate of revision surgeries and use of preventative measures (hemostatic nets, tissue sealant, and TXA). Meta-analyses were conducted to determine the probability of postoperative hematoma and the effectiveness of preventative measures. RESULTS: Overall, 8,841 patients from 31 studies were included. Ages ranged from 31 to 84 years, and 85.2% (4,330/5,080) were women. Meta-analysis showed an overall hematoma rate of 2.7% (95% CI: 2.2-3.4%), with major hematomas at 0.97% (95% CI: 0.61-1.53%). Subgroup analysis showed major hematoma rates of 1.53% with TXA, 1.25% with sealants, and 1.23% with hemostatic nets. CONCLUSIONS: Evidence from this review suggests that deep plane facelifts have a 2.7% overall and 0.97% major hematoma rate, with no clearly superior adjunct among TXA, sealants, or hemostatic nets.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".