Trends in the risk of second primary cancer among bladder cancer survivors: a population‐based cohort of 10 047 patients
Bibliographic record
Abstract
OBJECTIVES: To determine whether the risk of second primary cancer (SPC) among patients with bladder cancer (BCa) has changed over past years. MATERIALS AND METHODS: Data from 10 French population-based cancer registries were used to establish a cohort of 10 047 patients diagnosed with a first invasive (≥T1) BCa between 1989 and 2004 and followed up until 2007. An SPC was defined as the first subsequent primary cancer occurring at least 2 months after a BCa diagnosis. Standardized incidence ratios (SIRs) of metachronous SPC were calculated. Multivariate Poisson regression models were used to assess the direct effect of the year of BCa diagnosis on the risk of SPC. RESULTS: The risk of new malignancy among BCa survivors was 60% higher than in the general population (SIR 1.60, 95% confidence interval [CI] 1.51-1.68). Male patients presented a high risk of SPC of the lung (SIR 3.12), head and neck (SIR 2.19) and prostate (SIR 1.54). In multivariate analyses adjusted for gender, age at diagnosis and follow-up, a significant increase in the risk of SPC of the lung was observed over the calendar year of BCa diagnosis (P for linear trend 0.010), with an SIR increasing by 3.7% for each year (95% CI 0.9-6.6%); however, no particular trend was observed regarding the risk of SPC of the head and neck (P = 0.596) or the prostate (P = 0.518). CONCLUSIONS: As the risk of SPC of the lung increased between 1989 and 2004, this study contributes more evidence to support the promotion of tobacco smoking cessation interventions among patients with BCa.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".