Family History and Risk of Bladder Cancer: An Analysis Accounting for First- and Second-degree Relatives
Bibliographic record
Abstract
Although evidence suggests that a positive family history of bladder cancer in first-degree relatives is an important risk factor for bladder cancer occurrence, results remain unclear. The influence of family history of nonbladder cancers and more distant relatives on bladder cancer risk is inconsistent. This research, therefore, aims to increase the understanding of the association between family history and bladder cancer risk based on worldwide case-control studies. In total 4,327 cases and 8,948 non-cases were included. Pooled ORs, with corresponding 95% confidence intervals (CI), were obtained using multilevel logistic regression models, adjusted by age, sex, ethnicity, smoking status, and smoking pack-years. The results show bladder cancer risk increased by having a first- or second-degree relative affected with bladder cancer (OR, 2.72; 95% CI, 1.55-4.77 and OR, 1.71; 95% CI, 1.22-2.40, respectively), and nonurologic cancers (OR, 1.61; 95% CI, 1.19-2.18). Moreover, bladder cancer risk increased by number of cancers affected first-degree relatives (for 1 and >1 first-degree relatives: OR, 1.42; 95% CI, 1.02-2.04; OR, 2.67; 95% CI, 1.84-3.86, respectively). Our findings highlight an increased bladder cancer risk for a positive bladder cancer family history in first- and second-degree relatives, and indicate a possible greater effect for an increment of numbers of affected relatives. PREVENTION RELEVANCE: This study found a positive association between family history and bladder cancer in first- and second-degree relatives, with an added effect attributed to smoking. Given the detriments of bladder cancer, at-risk individuals should receive family history screening and tobacco cessation and avoidance counseling.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".