An Examination of Melanoma Detection and Characteristics at a Nova Scotia Tertiary Care Centre, From 2015-2019
Bibliographic record
Abstract
BACKGROUND: with the highest incidence in Nova Scotia (NS). OBJECTIVES: To describe the demographics, lesion characteristics, and diagnostic accuracy of suspected melanomas excised at the largest center in NS. METHODS: The dermatopathology database was interrogated for cases of possible melanoma from 2015 through 2019. Age, gender, site of lesion, pathologic diagnosis, Breslow depth, and equivocal pathology were assessed. RESULTS: 984 lesions had a clinical diagnosis of possible melanoma, identifying 301 melanomas. Of these, 142 (47%) were melanoma in situ (MIS) which in females occurred mostly on the extremities, while in males the head predominated. For invasive melanoma (IM), the extremities remained predominant for women, while the back was most common in men. Lower extremity lesions were more likely to be invasive and female patients were more likely to present with them at a younger age compared to males. The pathology was challenging for 23.94% of MIS, and 16.18% of IM. A mean of 3.1 lesions were excised for every melanoma identified. CONCLUSIONS: Early diagnosis of melanoma is challenging clinically and pathologically. Our melanoma detection rate was 31%, with an increasing trend in the proportion of MIS, and decreasing trend in the proportion of IM over the years. Almost 50% of melanomas were detected in early stages, supporting positive outcomes. Melanomas were more common on extremities in females and the back in males. Melanomas on the lower limbs were more likely to be invasive regardless of gender.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".