Preview long hair follicular unit excision: An up‐and‐coming technique
Bibliographic record
Abstract
BACKGROUND: Follicular unit excision (FUE) is a popular hair transplant technique, but requires shaving the donor area. This is a deterrent for some patients wishing to keep their hair transplant discreet. The new long hair FUE technique avoids shaving the donor area, which appeals to a wider patient population; however, it has a reputation of being technically challenging and slow. AIMS: We review the tools and techniques developed for long hair FUE and present our experience using the Trivellini Long Hair System and Long Hair punch. DISCUSSION: With the new advances in tools and techniques for long hair FUE, this method is gaining momentum and has the potential to be the next trend in the hair transplant industry. There are a few different punch designs marketed specifically for long hair FUE (window/slotted, Trivellini Long Hair, and bi-pronged). Although this technique is slower to perform than shaven FUE, graft survival and final outcome are comparable. CONCLUSIONS: Innovations in technology have made the long hair FUE technique more accessible to hair transplant surgeons. It is important for hair restoration surgeons to keep knowledgeable about this technique in order to maintain a competitive business.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".