Bibliographic record
Abstract
Over the past two decades, microvascular free tissue transfer has become a common procedure, usually with predictable results. The overall success rate of free flap surgery has gradually improved, and most recent reviews document an overall success rate between 90% and 95%. The goal of this study was to determine the outcome of those patients who underwent unsuccessful free microvascular tissue transfer. An extensive chart review was carried out on all those patients who underwent free microvascular tissue transfer at the Queen Elizabeth II Health Sciences Centre from 1988 to 1999. One hundred and forty-eight patients underwent free tissue transfer. A total of 164 free flaps were carried out, with an overall success rate of 92%. Free flap failure was defined as complete necrosis of the flap. There were 13 documented flap failures with complete necrosis of the flap requiring debridement. Seven patients underwent repeat free microvascular tissue transfer. These procedures were carried out by the same surgeon or surgical team 12 to 52 days after the first surgery. These secondary microvascular procedures were all successful. Of the remaining patients, five had either debridement and split-thickness skin grafting or regional flap coverage. One patient went on to lower limb amputation. This study reaffirms the success rates of free microvascular tissue transfer. In addition, the success rates of repeat free flaps were evaluated. It appears that patients undergoing repeat microvascular tissue transfers have no innate propensity for flap failure.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".