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
The popular skin classifications, notably the 'Fitzpatrick' and 'Obaji' classifications, are primarily based on skin colour. Other criteria are occasionally considered, such as the degree of skin oiliness, thickness, sensibility, etc. Although these classifications are easy to understand and apply, their simplicity limits their precision, sophistication and applicability.The new genetico-racial skin classification proposed herein suggests that skin response to any peel or laser treatment is genetically programmed and is, therefore, linked to the genetic and racial origin of the patient. In other words, in addition to skin colour, the patient's facial features and ancestry should be taken into account when classifying any skin.The new genetico-racial skin classification enables the physician to determine with great precision, and before any peel or laser treatment, the level of the patient's suitability and the expected postoperative outcomes; therefore, reducing the likelihood of complications.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".