Effect of Graft Size, Angle, and Intergraft Distance on Dense Packing in Hair Transplant
Bibliographic record
Abstract
BACKGROUND: The maximum number of hair grafts that can be safely implanted in 1 cm2 is still debatable. To our knowledge, no previous report has addressed this issue in three dimensions, taking into account the size, the angle of the graft, and the intergraft distance. OBJECTIVES: To study the effect of the size and angle of the graft and the intergraft distance on dense packing. METHODS: Using a mathematical formula (the maximum number of hair grafts in 1 cm2 = 33 * cosine), the volume of the recipient area and the volume of the hair graft are calculated, assuming that the surface area of the recipient area is 1 cm2, the diameter of the hair graft is 1 mm, and the intergraft distance is 1.5 mm laterally and 1 mm anteriorly and posteriorly. RESULTS: The maximum number of hair grafts that could be implanted in 1 cm2 at a 90 angle in relation to the skin surface is 33 grafts, at a 60 angle is 28 grafts, and at a 30 angle is 16 grafts. CONCLUSION: The maximum number of hair grafts that can be implanted in any given recipient area depends on the graft size, the angle or direction of these grafts, and the intergraft distance. Where more space is allowed between the grafts, and the more acute the angle, the fewer hair grafts that can be implanted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".