The edge out punch: An advancement that reduces transections in follicular unit excision hair transplantation
Bibliographic record
Abstract
BACKGROUND: The follicular unit excision (FUE) technique has become the preferred method for hair transplants over the traditional strip harvest technique due to low scar visibility and shorter recovery time for the patient. However, a limitation of the FUE technique is the potential for graft trauma due to the small diameter, sharp punches used to harvest individual follicular units. AIMS: Here, we introduce the novel edge out FUE punch that is designed with a thicker wall and has an internal bevel. We describe how the dynamics of this punch reduces the risk of follicle transection. METHODS: A review of the available literature and information on the edge out punch in comparison with other punch shapes, as well as the authors' experience in this area, is provided. RESULTS: The edge out punch is designed with thick walls and an internal bevel, placing the sharp cutting edge on the outer diameter. The dynamics of this punch aid in directing the graft into the center of the punch lumen and keeps the sharp cutting edge away from the hair follicles deeper in the dermis, reducing the risk of follicle transection. CONCLUSION: The dynamics of the forces generated by the edge out punch aid in minimizing follicular transections during graft harvesting. By understanding the dynamics behind this novel punch, hair restoration surgeons (HRSs) can optimize their surgical technique to obtain consistently high-quality grafts during FUE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".