Pre‐diagnostic circulating insulin‐like growth factor‐I and bladder cancer risk in the European Prospective Investigation into Cancer and Nutrition
Bibliographic record
Abstract
Previous in vitro and case–control studies have found an association between the insulin‐like growth factor (IGF)‐axis and bladder cancer risk. Circulating concentrations of IGF‐I have also been found to be associated with an increased risk of several cancer types; however, the relationship between pre‐diagnostic circulating IGF‐I concentrations and bladder cancer has never been studied prospectively. We investigated the association of pre‐diagnostic plasma concentrations of IGF‐I with risk of overall bladder cancer and urothelial cell carcinoma (UCC) in a case–control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. A total of 843 men and women diagnosed with bladder cancer between 1992 and 2005 were matched with 843 controls by recruitment centre, sex, age at recruitment, date of blood collection, duration of follow‐up, time of day and fasting status at blood collection using an incidence density sampling protocol. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using conditional logistic regression with adjustment for smoking status. No association was found between pre‐diagnostic circulating IGF‐I concentration and overall bladder cancer risk (adjusted OR for highest versus lowest fourth: 0.91, 95% CI: 0.66–1.24, ptrend = 0.40) or UCC (n of cases = 776; 0.91, 0.65–1.26, ptrend = 0.40). There was no significant evidence of heterogeneity in the association of IGF‐I with bladder cancer risk by tumour aggressiveness, sex, smoking status, or by time between blood collection and diagnosis (pheterogeneity > 0.05 for all). This first prospective study indicates no evidence of an association between plasma IGF‐I concentrations and bladder cancer risk.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".