The Incidence of Cutaneous Malignant Melanoma in Eastern Newfoundland and Labrador, Canada, from 2007 to 2015
Bibliographic record
Abstract
BACKGROUND: The incidence of cutaneous malignant melanoma continues to increase worldwide and in Canada. It is unclear whether the increase in incidence and clinical characteristic trends of cutaneous malignant melanoma are similar in the province of Newfoundland and Labrador. OBJECTIVE: The objective of this study is to examine the incidence and trends of cutaneous malignant melanoma in Eastern Newfoundland and Labrador. METHODS: Patients aged 18 years or older diagnosed with cutaneous malignant melanoma were identified from the Eastern Health Authority's Cancer Registry. The diagnosis was confirmed by a pathologist via histological subtype. Patients were excluded if the diagnosis was unspecified, a nonmelanoma skin cancer or if there was a recurrence in the same lesion location. In total 298 patients diagnosed with cutaneous malignant melanoma from 2007 to 2015 were included in the analysis. RESULTS: The total incidence increased from 4.1 to 15.6 cases/100,000 person-years, which represents a 283.0% increase from 2007 to 2015. The largest increases in incidence were seen in males and patients aged from 60 to 79 years. The most common lesion anatomical locations were the trunk in males and the lower extremity in females. The majority of cases had a Breslow thickness below 1.0 mm. CONCLUSION: The incidence of cutaneous malignant melanoma in Eastern Newfoundland and Labrador is increasing at a faster rate than in any other region in Canada. Health care providers should work to be aware of the clinical trends and risk factors associated with this disease to facilitate early detection and prevent morbidity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".