Detection Bias and Overestimation of Bladder Cancer Risk in Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To investigate whether the risk of bladder cancer in individuals with newly diagnosed type 2 diabetes is influenced by the frequency of physician visits before diagnosis as a measure of detection bias. RESEARCH DESIGN AND METHODS: With the use of linked administrative databases from 1996 to 2006, we established a cohort of 185,100 adults from British Columbia, Canada, with incident type 2 diabetes matched one to one with nondiabetic individuals on age, sex, and index date. Incidence rates and adjusted hazard ratios (aHRs) for bladder cancer were calculated during annual time windows following the index date. Analyses were stratified by number of physician visits in the 2 years before diabetes diagnosis and adjusted for age, sex, year of cohort entry, and socioeconomic status. RESULTS: The study population was 54% men and had an average age of 60.7±13.5 years; 1,171 new bladder cancers were diagnosed over a median follow-up of 4 years. In the first year after diabetes diagnosis, bladder cancer incidence in the diabetic cohort was 85.3 (95% CI 72.0-100.4) per 100,000 person-years and 66.1 (54.5-79.4) in the control cohort (aHR 1.30 [1.02-1.67], P=0.03). This first-year increased bladder cancer risk was limited to those with the fewest physician visits 2 years before the index date (≤12 visits, aHR 2.14 [1.29-3.55], P=0.003). After the first year, type 2 diabetes was not associated with bladder cancer. CONCLUSIONS: The results suggest that early detection bias may account for an overestimation in previously reported increased risks of bladder cancer associated with type 2 diabetes.
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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 | 0.084 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".