Trends in the incidence of cutaneous malignant melanoma in New South Wales, 1983-1996
Bibliographic record
Abstract
The incidence of cutaneous malignant melanoma (CMM) has been rising in fair-skinned populations throughout the world for decades. The upward trend may, however, finally be slowing in some of these populations. Recent (1983-1996) CMM incidence trends for a high incidence area (New South Wales, Australia) have been examined according to gender, age group, body site and tumour thickness. Despite continuing upward trends in older age groups, particularly among men (e.g., 7.20% increase per year in men aged 75+), incidence for younger ages is stabilizing (in men) or declining (in women): average annual percentage changes of -3.03 and -0.88 were observed for women aged 15-34 and 35-54, respectively. Patterns suggest a birth-cohort effect, with those born since 1945 or 1950 having lower (females) or similar (males) rates to those born earlier. For each gender, all-ages incidence rose by a similar amount for each of the main body sites except the leg in women, where incidence fell by 0.49% per year. In men, the incidence of both thin (</=75 mm) and thick (>75 mm) melanomas increased (significantly, by 2.63% per year and non-significantly, by 0.93% per year, respectively) between 1989 and 1996. In women, incidence remained stable for both thickness subgroups. These data are consistent with a stabilization or reduction in either total sun exposure or intermittency of exposure among New South Wales cohorts born since about 1950. Because incidence rates are still much higher than they were a few decades ago, however, efforts to reduce sun exposure, particularly in children and youth, must continue.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".