Hair transplantation Follicular Unit Excision (FUE): Introducing the multipurpose octagonal ring punch
Bibliographic record
Abstract
The technique of follicular unit excision for harvesting grafts for hair transplantation procedures has become very popular. This technique relies on the use of small punches to remove viable grafts. Many different punch shapes have been developed to accommodate the varying nature of skin and hair characteristics, resulting in hair transplant surgeons requiring a variety of punches to suit a wide range of patients, which can be overwhelming to the beginner when trying to decide on the optimal choice of a punch to suit a particular skin characteristic. We describe a novel multipurpose ring punch that can be used on patients with a variety of skin and hair characteristics, as well as for shaved and long hair FUE. Features of this punch include an octagonal ring that protrudes from the outer wall of the punch and functions to control the punch's trajectory into the deeper dermis during incision. Additionally, this punch has a dull, notched edge which allows for use in long hair and shaved FUE without sacrificing ease of incision through the epidermis. This punch is the first of its kind to have this breadth of versatility with a one-size-fits-all design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".