Cancer Survival and Excess Mortality Estimates among Adolescents and Young Adults in Western Australia, 1982–2004: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Data are limited on cancer outcomes in adolescents and young adults. METHODS: Based on data from the Western Australian Data Linkage System, this study modelled survival and excess mortality in all adolescents and young adults aged 15-39 years in Western Australia who had a diagnosis of cancer in the period 1982-2004. Relative survival and excess all-cause mortality for all cancers combined and for principal tumour subgroups were estimated, using the Ederer II method and generalised linear Poisson modelling, respectively. RESULTS: A cancer diagnosis in adolescents and young adults conferred substantial survival decrement. However, overall outcomes improved over calendar period (excess mortality hazard ratio [HR], latest versus earliest diagnostic period: 0.52, trend p<0.0001). Case fatality varied according to age group (HR, oldest versus youngest: 1.38, trend p<0.0001), sex (HR, female versus male: 0.66, 95% confidence interval [CI] 0.62-0.71), ethnicity (HR, Aboriginal versus others: 1.47, CI 1.23-1.76), geographical area (HR, rural/remote versus urban: 1.13, CI 1.04-1.23) and residential socioeconomic status (HR, lowest versus highest quartile: 1.14, trend p<0.05). Tumour subgroups differed substantially in frequency according to age group and sex, and were critical outcome determinants. CONCLUSIONS: Marked progressive calendar-time improvement in overall outcomes was evident. Further research is required to disentangle the contributions of tumour biology and health service factors to outcome disparities between ethno-demographic, geographic and socioeconomic subgroups of adolescents and young adults with cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".