High constant incidence rates of second primary cancers of the head and neck: A pooled analysis of 13 cancer registries
Bibliographic record
Abstract
Scanty data are available on the incidence (i.e., the absolute risk) of second cancers of the head and neck (HN) and its pattern with age. We investigated this issue using data from a multicentric study of 13 population-based cancer registries from Europe, Canada, Australia and Singapore for the years 1943-2000. A total of 99,257 patients had a first primary HN cancer (15,985 tongue, 22,378 mouth, 20,758 pharyngeal, and 40,190 laryngeal cancer), contributing to 489,855 person-years of follow-up. A total of 1,294 of the patients (1.3%) were diagnosed with second HN cancers (342 tongue, 345 mouth, 418 pharynx and 189 larynx). Male incidence rates of first HN cancer steeply increased from 0.68/100,000 at age 30-34 to 46.2/100,000 at age 70-74, and leveled off at older age; female incidence increased from 0.50/100,000 at age 30-34 to 16.5/100,000 at age 80-84. However, age-specific incidence of second HN cancers after a first HN cancer in men was around 200-300/100,000 between age 40-44 and age 70-74 and tended to decline at subsequent ages (150/100,000 at age 80-84); in women, incidence of second HN cancers was around 200-300/100,000 between age 45-49 and 80-84. The patterns of age-specific incidence were consistent for different subsites of second HN cancer and sexes; moreover, they were similar for age-specific incidence of first primary HN cancer in patients who subsequently developed a second HN cancer. The incidence of second HN cancers does not increase with age, but remains constant, or if anything, decreases with advancing age.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".