Long-Term Outcome after Successful Lower Extremity Free Flap Salvage
Bibliographic record
Abstract
BACKGROUND: Vascular occlusion after free flap surgery has become a rare complication but still poses a major challenge. It necessitates urgent re-exploration, but the logistic challenge to provide sufficient resources for the emergency intervention remains. The aim of this study was to analyze the long-term outcome after successful lower extremity free flap salvage. METHODS: A single-center retrospective study including long-term follow-up was approved by the local ethics committee. From January 1999 to December 2010, a total of 581 free flaps were performed for lower extremity reconstruction. Eighty-six flaps required emergency re-exploration, of which 65 could be salvaged. Fifteen salvaged flaps were excluded from the study because of secondary amputation. Of 50 patients, 29 (6 females and 23 males) were eligible for follow-up. The mean follow-up time was 54.5 ± 32.9 months. Health-related quality of life (Short Form 36 [SF-36]) and scar quality (Vancouver Scar Scale [VSS]) were analyzed. RESULTS: The overall flap survival rate was 94.7% and the total loss rate was 5.3%. The re-exploration rate was 14.8% (86 of 581 flaps). The salvage rate was 75.6% (65 of 86 flaps). Twenty-one free flaps were totally lost (24.4%). Partial flap loss occurred in 12 cases (14.0%); 67.5% of the vascular complications occurred during the first 24 hours, 20.9% between 24 and 72 hours, and 11.6% after more than 72 hours. The mean time from the first signs of impaired flap perfusion to re-exploration was 1.3 ± 0.4 hours, and from free tissue transfer to re-exploration was 16.2 ± 1.9 hours. The overall scar appearance was good with an average VSS score of 4.0 points. The average SF-36 physical component score was 54.4 ± 5.4 and the mental component score was 63.1 ± 10.7. CONCLUSION: Careful monitoring and the opportunity for urgent re-exploration are the key to success for free flaps salvage. Following these principles, an acceptable long-term outcome can be achieved.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".