Long-Term Hair Removal Using a 3-Millisecond Alexandrite Laser
Bibliographic record
Abstract
BACKGROUND: Laser epilation is now used widely as a clinical alternative to electrolysis for the removal of unwanted hair. All of the laser systems presently being used produce a reliable temporary hair loss by inducing telogen. Most of the published studies use follow-up periods of 6 months or less after the last treatment and cannot address the issue of permanency. Since many patients desire permanent hair loss, there is a need for specific information on the exact benefits and limitations of each particular system. OBJECTIVE: The purpose of this study was to assess the degree of hair loss attained by a single treatment with a 3-msec alexandrite laser. A designated period for follow-up was used to address the issue of long-term benefits. METHODS: A single treatment was carried out on 25 study sites with a 3-msec alexandrite laser at 755 nm using fluences of 30 to 50 joules/cm(2). Hair counts were obtained manually by two independent observers marking terminal hairs under magnification. The counts were repeated using photographic images and the average of the four readings taken. The degree of hair loss was calculated at a time after treatment equal to one complete growth cycle for the particular anatomic site. A second measurement was obtained at a time equal to one growth cycle plus 6 months to determine whether any hair loss had remained stable. RESULTS: The average hair loss at the first follow-up time was 43%, with 60% of sites showing a hair loss of >30%. The hair loss remained stable and the reduction in hair density at both designated times was statistically significant (p <.05). CONCLUSION: A normal-mode alexandrite laser achieves a long-term alopecia and may result in a permanent loss of terminal hair after one treatment at fluences of 30 to 50 joules/cm(2).
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".