Scalp and Forehead Reconstruction Using Free Revascularized Tissue Transfer
Bibliographic record
Abstract
OBJECTIVE: To examine the indications for, and the success of, free flap reconstruction in patients with forehead and scalp defects. DESIGN: Case series. SETTING: Two tertiary referral university teaching hospitals. Patients Twenty-six consecutive patients, aged 31 to 85 years, presenting with 26 scalp defects, 5 forehead defects, and 1 combined defect (size, 70-672 cm(2)). Three patients required resection and repair of the dura at surgery. Intervention Patients were staged according to the size of the defect and the viability of surrounding tissue; free flap reconstruction was performed where indicated. MAIN OUTCOME MEASURES: Flap survival, complications, and disease-free and overall survival. RESULTS: Thirty-four free flap reconstructions were performed (24 latissimus dorsi free flaps, 4 scapular free flaps, 3 rectus abdominis free flaps, and 3 radial forearm free flaps). One failed 2 weeks postoperatively, and 2 required exploration (1 for arterial ischemia and 1 for a hematoma). There were 3 cases of donor site morbidity (2 early seromas and 1 late abdominal hernia). One patient died of a pulmonary embolus 1 week postoperatively. Disease-free survival was 48% at 5 years and overall survival was 59% at 5 years, with a median follow-up of 24 months. CONCLUSIONS: Free revascularized tissue transfer is a reliable and safe way of reconstructing large scalp or forehead defects after traumatic injury or neoplastic resection. The muscle-only latissimus dorsi free flap for scalp reconstruction and the cutaneous scapular free flap for the forehead have proved successful in selected patients with a low complication rate and satisfactory cosmesis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".