Follicular Unit Excision Punches and Devices
Bibliographic record
Abstract
BACKGROUND: Follicular unit excision (FUE) is used to harvest follicular units for hair transplantation using trephine punches. The characteristics of FUE punches can impact the success of this technique; thus, many innovative punch designs and devices have been developed. With many options available, it can be difficult for the hair restoration surgeon to know which punch best suits the needs of their patients. OBJECTIVE: To provide a comprehensive review of punch shapes and devices available. METHODS: Search of PubMed, reference mining of relevant publications, and hand searching trade publications. RESULTS: We examined FUE punches and devices and consolidated descriptive information for each to create textual and visual guides. No single punch shape or device may suit all cases; thus, it is important to know the best uses and limitations of each. CONCLUSION: The surgeon should have a comprehensive knowledge base of available punch shapes and devices and understand the advantages and disadvantages of each. It is also beneficial to have an in-depth knowledge of skin properties and follicular unit structure. Ultimately, understanding the dynamics behind punch excision will enhance the FUE technique.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 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) | 0.010 | 0.003 |
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".