Scalp reconstruction after oncologic resection: A retrospective comparative study of acellular dermal matrices and local flap
Bibliographic record
Abstract
Scalp reconstruction following oncologic resection presents significant challenges due to limited tissue mobility, functional requirements, and aesthetic considerations. Traditional approaches include local flaps, which provide vascularized, durable coverage but may involve longer operative times and donor site morbidity. Acellular dermal matrices (ADM) offer a staged, less invasive alternative, potentially suitable for patients with limited donor options or reduced surgical tolerance. This study compares ADM and local flaps in terms of healing, complications, and aesthetic outcomes, with the aim of identifying clinical contexts in which each technique may be preferred. This retrospective case-control study included 45 patients undergoing scalp reconstruction after wide local excision of skin tumors. Patients were divided into two groups: ADM-based reconstruction and local flap-based reconstruction. Primary outcomes included wound healing time, complication rates, and aesthetic results (Vancouver Scar Scale and Patient Satisfaction Scores). Secondary outcomes included operative time, hospital stay, and need for secondary procedures. Statistical comparisons used t-tests and Fisher’s exact test (p < 0.05). Flap reconstruction resulted in faster wound healing (23 ± 4 days vs. 72 ± 8 days; p < 0.05) and shorter total operative time (59 ± 5 vs. 77 ± 7.6 minutes; p < 0.05), although ADM allowed for shorter first-stage procedures. ADM was associated with a higher rate of localized infection (19%), while flaps more frequently showed partial necrosis (16%). Aesthetic outcomes and patient satisfaction were comparable. Complication rates did not differ significantly by age. Hair loss did not affect satisfaction in the ADM group, which included mainly older patients. Both ADM and local flaps are effective reconstructive options, each with distinct advantages. ADM may be preferable in frail patients or when donor site availability is limited, while flaps remain the standard for large or high-tension defects. Rather than competing, these techniques should be viewed as complementary tools within a patient-specific reconstructive strategy. Further studies are warranted to refine selection criteria and explore hybrid approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".