Head and Neck Restoration in Scar Alopecia: Hair Transplantation in Scalp, Eyebrows, Beard and Mustache.
Bibliographic record
Abstract
BACKGROUND: Surgical management of hair loss has become an increasingly challenging procedure, when dealing with scar alopecia. We investigated the efficacy of hair transplantation in patients with head and neck scar alopecia. METHODS: From 2016 to 2018 in Shiraz, Iran, all patients with scar alopecia in head and neck were evaluated for efficacy of follicular unit extraction (FUE), follicular unit transplantation (FUT) or a combination of two methods from donor sites in scalp and beard various hair-grafts were compared. RESULTS: Fifty-six patients were enrolled. Most of them were between 31 and 40 yr old (48.3%) and male (71.4%). Trauma, burn, surgical excision of adjacent skin, radiotherapy and leishmaniasis were the registered causes. Scars were visible in scalp (39.3%), beard (28.6%), eyebrow (21.4%), and moustache (10.7%) regions. FUE (87.5%), FUT (10.7%) and a combination (1.8%) were the used methods. One-hair-grafts were used in eyebrows (100%), moustache (100%), beard (88%) and scalp (7.9%), while 2-hair-grafts in beard (6%) and scalp (47.4%) and 3-haired grafts in beard (6%) and scalp (44.7%) transplantations. CONCLUSION: In head and neck scar alopecia, hair transplantation was selected based on type and depth of scar. FUE was targeted when huge grafts were not needed, including beard, moustache, and eyebrow, while FIT was used when extensive scars were present in scalp. One-hair-grafts were mostly applied for eyebrow, moustache and beard, 2-hair- and 3-hair- grafts for beard and scalp transplantation. These findings can be added to the literature when FUE, FIT, or their combination are targeted in hair restoration of scar alopecia in head and neck.
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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 | 0.000 |
| 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) | 0.001 | 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".