Digital Dermoscopy Photographs Outperform Handheld Dermoscopy in Melanoma Diagnosis
Bibliographic record
Abstract
BACKGROUND: Pigmented lesion clinics (PLCs) that use technology such as digital dermoscopy and total-body photography are thought to confer a clinical advantage for patients at high risk of developing melanoma over general dermatology clinics (GDCs) with regular dermoscopy. OBJECTIVE: To examine the difference between depths of melanomas diagnosed in a PLC and a GDC. METHODS: Medical records from 257 patients at the PLC at The Ottawa Hospital and 441 patients from a GDC were reviewed. RESULTS: Invasive melanoma was less frequent than in situ melanoma at the PLC (7.14% vs 38.27%; P = .02). The average Breslow depth for melanomas at the PLC was also smaller compared with the GDC (0.0371 vs 0.3450 mm; P = .02). CONCLUSIONS: The use of digital dermoscopy and total-body photography together in a PLC appears to be an effective way to monitor patients at high risk of melanoma.
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".