Donor Site Closure in Paramedian Forehead Flap Reconstruction: Impact on Scar Quality and Patient Satisfaction
Bibliographic record
Abstract
BACKGROUND: The paramedian forehead flap (PMFF) is a longstanding, versatile technique used in nasal reconstruction offering robust vascularity and excellent aesthetic outcomes. While the donor site is usually closed primarily, tension can make this challenging and may create widened scarring or dehiscence. METHODS: A retrospective review of 108 patients undergoing PMFF following an excision of a basal or squamous cell carcinoma between January 2015 and December 2023 by a single surgeon was performed. Size of defect, location, postoperative complications, scar quality, and analysis using the validated Patient and Observer Scar Assessment Scale (POSAS) were used to assess patient satisfaction with their donor site scar. RESULTS: The PMFF donor site was closed primarily in 50.9% of patients, with a skin graft in 16.7% and with a hemicoronal flap in 32.4%. Primary closure exhibited higher complication rates (wound dehiscence, alopecia) in medium defects (2-4 cm) and worse cosmesis (POSAS 40) compared with small defects <2 cm (POSAS 15). Skin grafting showed the worst cosmesis (POSAS 42) despite no complications. The hemicoronal flap group demonstrated the best cosmesis (POSAS 9 for medium defects and 14 for large (>4 cm) defects) and no complications, suggesting superior scar quality and minimal tension. CONCLUSION: The hemicoronal flap shows promise in decreasing wound tension and improving the scar quality in PMFF. This helps decrease complications associated with high wound tension as well as increase patient satisfaction in their scar.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".