Clinical Recognition of Melanoma in Dermatologists and Nondermatologists
Bibliographic record
Abstract
BACKGROUND: The incidence of melanoma is increasing annually in Canada. OBJECTIVES: This retrospective study was designed to assess the ability of physicians of different specialties to accurately recognize melanoma. METHODS: Pathology reports of biopsies submitted to Vancouver Coastal Health with clinical diagnoses of melanoma were reviewed (January to July 2013). The clinical diagnoses made by dermatologists, general practitioners and family physicians, and all other specialists were correlated with the final histopathologic diagnoses. RESULTS: The dermatologists, general practitioners and family physicians, and all other specialists achieved diagnostic accuracies of 24.75%, 3.52%, and 12.75%, respectively. CONCLUSIONS: Although the diagnostic accuracy of dermatologists was significantly better than that the other practitioners, the majority of patients with suspicious skin lesions present to family physicians or general practitioners first. Thus, there is considerable value in providing more training and education to nondermatologists, because it can have a meaningful impact on patient care.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".