Risk of second primary cancer among patients with head and neck cancers: A pooled analysis of 13 cancer registries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it