Secondary Intention Healing Over Exposed Bone on the Scalp, Forehead, and Temple Following Mohs Micrographic Surgery
Bibliographic record
Abstract
BACKGROUND: Removal of skin cancers on the scalp, forehead, and temple can result in surgical defects with exposed bone. In such cases, reconstruction becomes challenging due to limited vascularity for flap or graft repair. OBJECTIVE: Demonstrate the usefulness of secondary intention healing of scalp, forehead, and temple defects over exposed bone. METHODS/MATERIALS: A retrospective case series of 41 patients who had Mohs Micrographic Surgery with post-surgical scalp, forehead, or temple defects involving exposed bone. These patients then underwent secondary intention healing. RESULTS: 90% of patients successfully healed. Average time to complete granulation was 92 days, and average time to full re-epithelialization was 186 days. Visual analog scale assessment of final scar quality resulted in 57% being good, 35% being fair, and 8% being poor. No patient had infection or other serious complication. Mean follow-up duration was 272 days. CONCLUSION: This case series shows the viability of secondary intention healing of scalp wounds over exposed bone. Study power was not adequate to predict time to complete healing based on defect size, or allow association of patient factors with the risk of nonhealing. Managing patient expectations, and emphasizing the importance of early occlusive wound care is paramount for healing success.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".