Incidence of multiple primary cancers following respiratory tract cancer in Umbria, Italy.
Bibliographic record
Abstract
UNLABELLED: Improvements in cancer survival and life expectancy have placed a focus on long-term risks following a primary cancer, including that of developing other primary malignancies. The purpose of this study was to evaluate the risk, in patients with respiratory tract cancers, of developing a second primary malignancy. METHODS: Standardized incidence ratios (SIR) of observed to expected cases were calculated for residents of Umbria diagnosed with laryngeal and lung cancer between 1994 and 2008. Significance and 95% confidence intervals were determined assuming a Poisson distribution. RESULTS: In total, 189 and 340 cases of second primary cancers were observed respectively among laryngeal and lung cancer patients. Male laryngeal cancer patients were found to have a significantly increased risk of lung cancer (SIR=4.10), non-melanoma skin cancer (SIR=2.10), bladder cancer (SIR=2.25) and pancreatic cancer (SIR=3.85). In females, a significantly increased risk was observed only when all sites combined were considered. Male lung cancer patients were found to have a significantly increased risk for laryngeal cancer (SIR=4.36), esophageal cancer (SIR=3.97), kidney cancer (SIR=3.40), multiple myeloma and malignant plasma cell neoplasm (SIR=2.97), bladder cancer (SIR=2.20) and non-melanoma skin cancer (SIR=1.55). In females, the risk of developing a second cancer was higher but was not significant for non-melanoma skin cancers, colon and breast cancer. CONCLUSIONS: Study results show an excess risk of other primary malignancies in respiratory tract cancer patients, particularly males. This may be due to shared risk factors, genetic susceptibility, effect of first cancer treatments and increased diagnostic surveillance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".