Risk of second primary cancer among patients with head and neck cancers: A pooled analysis of 13 cancer registries
Bibliographic record
Abstract
The objective of the study was to assess the risk of second primary cancers (SPCs) following a primary head and neck cancer (oral cavity, pharynx and larynx) and the risk of head and neck cancer as a SPC. The present investigation is a multicenter study from 13 population-based cancer registries. The study population involved 99,257 patients with a first primary head and neck cancer and contributed 489,855 person-years of follow-up. To assess the excess risk of SPCs following head and neck cancers, we calculated standardized incidence ratios (SIRs) by dividing the observed numbers of SPCs by the expected number of cancers calculated from accumulated person-years and the age-, sex- and calendar period-specific first primary cancer incidence rates in each of the cancer registries. During the observation period, there were 10,826 cases of SPCs after head and neck cancer. For all cancer sites combined, the SIR of SPCs was 1.86 (95% CI = 1.83-1.90) and the 20-year cumulative risk was 36%. Lung cancer contributed to the highest proportion of the SPCs with a 20-year cumulative risk of 13%. Excess second head and neck cancer risk was observed 10 years after diagnosis with lymphohaematopoietic cancers. The most common SPC following a first primary head and neck cancer was lung cancer. However, the highest excess of SPCs was in the head and neck region. These patterns were consistent with the notion that the pattern of cancer in survivors of head and neck cancer is dominated by the effect of tobacco smoking and alcohol drinking.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".