Skin Characteristics of Hairdresser Apprentices at the Beginning of Vocational Training
Bibliographic record
Abstract
BACKGROUND: Hairdresser apprentices (HAs) are at high risk of developing occupational contact dermatitis. OBJECTIVES: To assess skin characteristics of HAs, using genotyping, clinically observed and self-reported skin symptoms, and skin bioengineering methods at the beginning of apprenticeship. METHODS: During the screening phase of a prospective cohort study, we recruited 352 HAs in 24 Croatian towns. The protocol included the following: questionnaires with self-reported skin and atopy symptoms evaluation, Osnabrueck Hand Eczema Severity Index (OHSI) for clinical skin assessment, genotyping FLG (filaggrin) gene mutations, skin pH, and transepidermal water loss (TEWL) measurements. RESULTS: Self-reported skin symptoms were reported by 12%, history of dry hands by 29%, and history of atopy by 46% of HAs. Skin changes were found at the clinical examination in 18% of the HA. The OHSI score was positively correlated with hand TEWL and hand skin pH in multiple regression linear models. An FLG gene mutation was found in 1 apprentice. CONCLUSIONS: Significant prevalence of clinically observed skin signs on the hands was observed in HAs at the beginning of training. The OHSI score was found to be an independent predictor of higher hand TEWL and skin pH values. The need to ameliorate preventive examinations before the enrolment to hairdressing schools was indicated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".