Bibliographic record
Abstract
BACKGROUND: There are several methods for harvesting from donor area, including punch graft, multiple bladed knife, or single bladed knife excision followed by excision of an ellipse. Vertical harvesting is excellent for avoiding follicular transections because of complete visualization of the angle of the hair exiting the scalp. Also, the donor area is easier to visualize because of rock-hard tumescence achieved during the totality of procedure. Only an experienced surgeon using a multiple bladed knife would have fewer follicular transections. OBJECTIVE: Optimal yield of the donor area: ie, minimal transection, thus less hair follicle transection. METHOD: Vertical harvesting of slivers of hair measuring 0.75-1.85 cm in height, and 3-4 millimeters in width using #10 solitary bladed knife and Optivisor (as magnification). RESULT: In our opinion, there is less transection of the follicular bulbs leading to an increase in yield. CONCLUSION: Patient comfort is maximized because the patient lies on one side. Another advantage is the reduced role of the technician, therefore requiring fewer technicians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".