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Record W2027244873 · doi:10.1002/ijc.1203

Trends in the incidence of cutaneous malignant melanoma in New South Wales, 1983-1996

2001· article· en· W2027244873 on OpenAlexaff
Loraine D. Marrett, Huu L. Nguyen, Bruce K. Armstrong

Bibliographic record

VenueInternational Journal of Cancer · 2001
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsCancer Care OntarioUniversity of Toronto
Fundersnot available
KeywordsIncidence (geometry)DemographyMedicineCohortMelanomaInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.374

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.020
GPT teacher head0.312
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations148
Published2001
Admission routes1
Has abstractyes

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