Trends in Invasive Cutaneous Melanoma in Saskatchewan 1970?1999
Bibliographic record
Abstract
BACKGROUND: Melanoma incidence rates have increased dramatically in white populations worldwide during the past several decades. A more modest increase has been observed for melanoma-related mortality. Cause-specific and disease-free survivals are related to tumor characteristics, gender, age, and possible anatomic site. It is difficult to accurately assess these trends without information on tumor thickness that is often unavailable. OBJECTIVE: This study determines trends in melanoma incidence, mortality, and survival in Saskatchewan for a 30-year period, incorporating analysis of tumor thickness. METHODS: Information about cases of primary cutaneous melanoma for the 30-year period 1970-1999 was obtained from the population-based Saskatchewan Cancer Registry. A 50% random sample of charts was reviewed to collect information about Breslow depth, Clark level, and other demographic data not available from the Registry. Multivariate regression analysis was used to determine the significance of prognostic factors on incidence and five-year relative survival rates. RESULTS: The number of patients registered increased dramatically during the study period. The increase was greatest for thin lesions in all age groups. Anatomic site varied by gender. Head and neck tumors showed continual increase in risk with increasing age. Mortality rates in females have been stable over time but increased for males in the 1990s. The prognostic factors tht predicted excess mortality at five years were tumor thickness, Clark level, and gender. CONCLUSION: The observed increase in melanoma appears to be real and not the result of increased surveillance or screening. Tumor characteristic (Breslow depth, Clark level) and gender were significant prognostic indicators of five-year excess mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".