Double Free-Flap Reconstruction
Bibliographic record
Abstract
OBJECTIVE: To investigate the increasing use of double free flaps in the reconstruction of large head and neck defects. DESIGN: A 5-year retrospective medical record review in a large tertiary care head and neck oncology program. Prospectively collected functional data were also analyzed. SETTING: Academic research. PATIENTS: A consecutive series of 35 patients (24 men and 11 women; mean age, 57.7 years). MAIN OUTCOME MEASURES: The use of double free flaps in the reconstruction of large head and neck defects and prospective functional outcomes. RESULTS: The most common indication for surgery (n = 25 [71.4%]) was squamous cell carcinoma. The most common double free-flap combination (n = 22 [62.9%]) included an osteocutaneous fibular free flap with a fasciocutaneous radial forearm free flap. Objective evaluation by naive listeners demonstrated a mean single-word intelligibility score of 66.2% and a mean sentence intelligibility score of 84.8% in this group of patients. Modified barium swallow study results revealed no evidence of laryngeal penetration for swallowing liquid consistencies in 21 patients (60.0%), pudding consistencies in 30 patients (85.7%), and cookie consistencies in 32 patients (91.4%). CONCLUSIONS: With proper patient selection and planning and the use of 2 surgical teams, the length of surgery and complication rates are not significantly increased in double free-flap reconstruction. Furthermore, by using 2 free flaps, the best osseous and soft-tissue elements may be independently selected, yielding appropriate tissue characteristics for ideal defect reconstruction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".