Pioglitazone and the risk of prostate cancer.
Bibliographic record
Abstract
e16574 Background: Data from recent studies have associated the use of pioglitazone, an antidiabetic drug of the thiazolidinedione (TZD) class, with an increased risk of prostate cancer (PCa), but the evidence remains limited. Additional studies are needed to examine this possible association. Methods: Using the UK Clinical Practice Research Datalink, we identified all male patients initiating an antidiabetic drug between 2000 and 2013; patients were followed through 2014. The use of pioglitazone was treated as a time-varying variable, lagged by one year for latency purposes. Time-dependent Cox proportional hazards models were used to estimate adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) of PCa associated with the use of pioglitazone, compared with non-TZD use. Secondary analyses were conducted to assess the association by tertile categories of cumulative duration and cumulative dose. Results: The cohort included 81,206 patients, of whom 1344 were newly-diagnosed with PCa (rate: 332/100,000 person-years). The use of pioglitazone was not associated with an overall increased risk of PCa (HR: 1.10, 95% CI: 0.90-1.34). In secondary analyses, non-linear trends were observed with cumulative duration and cumulative dose (Table). In the latter, the second tertile was associated with a significant increased risk of PCa (HR: 1.62, 95% CI: 1.18-2.22), whereas the third tertile was associated with a decreased risk (HR: 0.45, 95% CI: 0.26-0.81). Conclusions: The use of pioglitazone was not associated with an overall increased risk of PCa, and the inconsistent duration- and dose-response relationships suggest a non-causal relationship. Hazard ratios for pioglitazone and prostate cancer risk by cumulative duration and dose. Pioglitazone use category Cumulative dura tion (days) Adjusted HR (95% CI) Cumulative dose (mg) Adjusted HR (95% CI) Tertile 1 1-480 1.24 (0.87- 1.77) 1-11,220 1.17 (0.82- 1.68) Tertile 2 481-1035 1.36 (0.96- 1.92) 11,221- 27,945 1.62 (1.18- 2.22) Tertile 3 ≥ 1036 0.65 (0.40- 1.04) ≥ 27,946 0.45 (0.26- 0.81)
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".