One Versus 2 Venous Anastomoses in Free Flap Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The necessity of a second venous anastomosis in free flap surgery is controversial. The purpose of this systematic review is to determine whether venous flap failure and reoperation rates are lower when 2 venous anastomoses are performed. The secondary objective is to determine whether venous flap failure and reoperation rates are lower when the 2 veins are from 2 different drainage systems. METHODS: A comprehensive search of the literature identified relevant studies. Investigators independently extracted data on rates of flap failure and reoperation secondary to venous congestion. A meta-analysis was performed; odds ratios (ORs) were pooled using a random-effects model and 95% confidence intervals (CIs). RESULTS: Of 18 190 studies identified, 15 were included for analysis. The mean sample size was 287 patients (minimum = 102, maximum = 564). No statistically significant difference in venous flap failure was found when comparing 1 versus 2 venous anastomoses (OR: 1.35; 95% CI: 0.46-3.93). A significant decrease in reoperation rate due to venous congestion was shown (OR: 3.03; 95% CI: 1.64-5.58). The results favor using 2 veins from 2 different systems over veins from the same system (OR: 0.16; 95% CI: 0.02-1.27). CONCLUSIONS: There is low-quality evidence suggesting that the use of 2 venous anastomoses will lower the rate of reoperation due to venous congestion. There are insufficient data published to meaningfully compare outcomes of flaps with 2 venous anastomoses from different systems to flaps with anastomoses from the same system.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".