The new Genetico-Racial Skin Classification: How to maximize the safety of any peel or laser treatment on any Asian, Caucasian or Black patient
Bibliographic record
Abstract
T raditional skin classifications (1-5), such as the 'Fitzpatrick' and 'Obagi' classifications, are primarily based on skin colour.The prevailing general assumption, reigning in dermatological and plastic surgery circles, is that the result of a peel, laser or dermabrasion treatment is directly related to the intensity of the skin colour: the lighter the skin, the better the result; the darker the skin, the poorer the result.This oversimplification, although attractive at first, is inadequate for providing much needed answers to the likely response and risks of each skin category to the treatments.In fact, in certain instances, these classical classifications may be misleading and even counterproductive.The new genetico-racial skin classification, first introduced by the senior author (6-12), suggests that taking the racial origin of the patients into consideration enables the physician to predict with precision the suitability, risks and outcome of a skin treatment before it is performed on a specific patient.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".