Old Friend or New Ally: A Comparison of Follicular Unit Transplantation and Follicular Unit Excision Methods in Hair Transplantation
Bibliographic record
Abstract
BACKGROUND: Follicular unit excision (FUE) and follicular unit transplantation using strip surgery (FUT) are the dominant graft harvest methods in hair transplantation. The increase in the demand for FUE has reignited the debate of the relative superiority of the 2 methods. OBJECTIVE: To present a critical comparison of FUE and FUT graft harvesting techniques. MATERIALS AND METHODS: Search of PubMed, trade publications, and printed references. RESULTS: Follicular unit excision and FUT methods provide high-quality grafts, but differ in their scarring patterns of the donor region. Follicular unit transplantation results in a linear scar, whereas FUE produces punctate scars that are typically easily concealed. Distinct subgroups of hair transplant patients are eligible for FUE, FUT, or both procedures. CONLCUSION: Both FUE and FUT are equally effective in generating high-quality grafts. This detailed evaluation of the FUT and FUE procedures will assist hair restoration surgeons make informed decisions about the best approach for their patients.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".