A Ten-Year Experience of Multiple Flaps in Head and Neck Surgery: How Successful Are They?
Bibliographic record
Abstract
Ablative surgery in the head and neck often results in defects that require free flap reconstruction. With improved ablation/reconstructive and adjuvant techniques, improved survival has led to an increase in the number of patients undergoing multiple free flap reconstruction. We retrospectively analyzed a single institution's 10-year experience (August 1993 to August 2003) in free flap reconstruction for malignant tumors of the head and neck. Five hundred eighty-two flaps in 534 patients were identified with full details regarding ablation and reconstruction with a minimum of 6-month follow-up. Of these 584 flaps, 506 were for primary reconstruction, 50 for secondary reconstruction, 12 for tertiary reconstruction, and 8 patients underwent two flaps simultaneously for extensive defects. Overall flap success was 550/584 (94%). For primary free flap surgery, success was 481/506 (95%), compared with 44/50 (88%) for a second free flap reconstruction and 9/12 (75%) for a third free flap reconstruction ( P < 0.05). Eight extensive defects were reconstructed with 16 flaps, all of which were successful. More than one free flap may be required for reconstruction of head and neck defects, although success decreases as the number of reconstructive procedures increases.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".