Dermatologist and family practitioner practice patterns for occupational contact dermatitis
Bibliographic record
Abstract
Medical practitioners have a role in the recognition of occupational contact dermatitis. The longer the duration of symptoms before diagnosis, the poorer the outcome. Our objective was to understand practice patterns, barriers and needs for early diagnosis of occupational contact dermatitis. A survey to obtain information on practice patterns was developed based on the literature and interviews with dermatologists and family practitioners. The survey was sent to all dermatologists and a random sample of 600 family practitioners in Ontario. Fifty-seven per cent of dermatologists and 9% of family practitioners report seeing more than 20 patients per year with occupational contact dermatitis. The majority of practitioners report taking a workplace exposure history. Barriers to taking a workplace exposure history include time constraints and lack of knowledge. Reasons for referral to specialists include a lack of expertise, testing facilities and knowledge about workers' compensation, time constraints and inadequate reimbursement, whereas lack of access to specialists is a barrier for referral. Although most practitioners identify a need for further education, a low volume of patients and time constraints are key barriers to continuing education. Opportunities are identified to improve educational initiatives and health services delivery for occupational contact dermatitis, tailored to each practitioner group.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".