Clues to the aetiological heterogeneity of testicular seminomas and non-seminomas: time trends and age-period-cohort effects
Bibliographic record
Abstract
BACKGROUND: Most previous epidemiological studies have treated testicular cancer as a single entity. However, some investigators suggest that testicular seminomas and non-seminomas may have different risk profiles. We examine the time trends in incidence of the two main histological types separately. METHODS: From 1970 through 1995, 7296 cases of testicular cancer were registered in the Canadian provinces of Ontario, Saskatchewan and British Columbia. In addition to analyses of the secular trends by age group and birth cohort, an age-period-cohort (APC) model with standard Poisson assumptions was fitted to the data to assess the time effects. RESULTS: The age-adjusted incidence rate for seminomas increased by 53%, from 1.5 per 100 000 males in 1970-1971 to 2.3 per 100 000 males in 1994-1995. Non-seminomas increased by 91%, from 1.1 to 2.1 per 100 000 males over the same period. Non-seminomas were more frequent at young ages whereas seminomas dominated in older ages. In contrast to seminomas, non-seminomas occurred predominantly among adolescent men (15-19 years), with a fourfold increase between 1970-1971 and 1994-1995. Age-period-cohort modelling showed that the increase in the risk of both seminomas and non-seminomas followed a birth cohort pattern, but with differences in birth cohorts in addition to significantly distinct age patterns. CONCLUSIONS: Our findings support the hypothesis postulating aetiological heterogeneity in the development of seminomas and non-seminomas. We suggest that epidemiological studies of testicular cancer treat seminomas and non-seminomas separately.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".