Estimating Occupational Exposure to Ultraviolet Radiation Using the Canadian Occupation Exposure Matrix (CANJEM) in a Nationwide Cohort of French Adults
Bibliographic record
Abstract
Background: According to the World Health Organization (WHO), one in three cancers diagnosed is a skin cancer (1), and the incidence is expected to further rise in Europe by 2040 (2).WHO acknowledges the relevance of early diagnosis programmes to reduce the proportion of patients with late-stage skin cancer diagnoses, with improved healthcare accessibility as a key component (3).Currently, dermatologists have long waiting lists, and general practitioners do not always feel comfortable evaluating skin lesions.Considering the anticipated rise in skin cancers, safeguarding timely diagnosis and treatment poses a significant challenge.To address this challenge, we implemented a nurse-initiated one-spot-check consultation to increase dermatology capacity and accessibility.Methods: The Dermatology Department of University Hospital Ghent offered nurse-initiated early-access consultations to adult patients with concerns about specific lesions meeting predefined criteria.To optimise these consultations, nurses received training on skin tumours and basic dermoscopy and gained expertise through clinical apprenticeship, close guidance and feedback.A dedicated patient file tab was developed to streamline operations and reduce administrative workload.During these consultations, nurses performed patient assessments, clinical and dermoscopic examinations, lesion imaging, completed patient files, and executed necessary management actions.The diagnosis and management strategy were established under the supervision of a dermatologist.Before dermatologist supervision, nurses assessed the skin lesions as high or low risk for cancer.These assessments were compared with the dermatologist's final diagnosis to evaluate the nurses' diagnostic accuracy.Results: From April 2021 until April 2023, 1183 patients received a nurse-initiated one-spot-check consultation, yielding a 10% detection rate of skin cancer.179 lesions were included to determine the nurses' diagnostic accuracy.The results revealed a sensitivity range of 73 to 81% and a specificity range of 88 to 90%, illustrating the variability in diagnostic accuracy associated with nurse experience.Conclusions: Preliminary findings suggest that nurses, with appropriate education and training, can acquire competence in differentiating benign and malignant skin lesions.Implementing nurse-initiated consultations in the department has increased operational capacity, improving early access to dermatological advice for individuals with suspicious skin lesions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".