Incidence Rate of Hand Eczema in Different Occupations: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Hand eczema is a chronic disease that results in economic and psychosocial burdens. The aim of this study was to systematically review and assess the magnitude of the association between exposure related to occupations and the incidence rate of hand eczema. A systematic search in PubMed, EMBASE, CINAHL and Cochrane databases, from inception to September 2017, of full-text observational studies reporting incident cases of hand eczema during employment, and a supplementary search in PubMed to September 2020, were conducted. Among 2,417 screened abstracts, 15 studies fulfilled the inclusion criteria. Incidence rates were reported per 100 person-years. Based on the Newcastle-Ottawa Scale, 9 studies were good quality, 2 fair quality, and 4 poor quality. Hairdressers had a high incidence of hand eczema of 21.4 (95% confidence interval [CI] 15.3-27.4), as did nurses, 16.9 (95% CI 11.2-22.7), and metal workers, 12.4 (95% CI 3.5-21.3). Hairdressers were predominantly women, and metal worker were predominantly men. Office occupations had an incidence rate of hand eczema of 4.9 (95% CI 1.2-9.6). The high risk of hand eczema for hairdressers, nurses, and metal workers, should be considered by healthcare policymakers. Even occupations with low irritant profile, such as office workers, were at risk of developing hand eczema, and more occupations should be investigated regarding the related risk of developing hand eczema.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".