The deleterious association between proton pump inhibitors and prostate cancer specific death.
Bibliographic record
Abstract
309 Background: Proton pump inhibitors (PPIs) are a commonly prescribed class of medications. Although in-vitro and in-vivo data have shown PPIs to have anti-tumor effects, more recent studies suggest an increased cancer risk in several solid organs. Pantoprazole, a commonly prescribed PPI, has been shown to harbor a protective effect in human prostate cancer (PCa) cells. We aimed to investigate the effect of pantoprazole and other PPIs on PCa-specific death and additional PCa outcomes. Methods: In this retrospective, population-based cohort study, data were incorporated from the Institute for Clinical and Evaluative Sciences to identify all men aged 66 and above with a history of a single negative prostate biopsy between 1994 and 2016. We used multivariable Cox regression models with time-dependent covariates, to assess the effect of PPIs on PCa diagnosis, androgen deprivation therapy (ADT) use, and PCa-specific death. All models included other medications with a putative effect on PCa. All models were adjusted for age, rurality, comorbidity, and year of patient study inclusion. Results: Overall, 21,512 men were included, with a mean follow-up time of 8.06 years (SD 5.44 years). A total of 10,999 patients (51.1%) used a PPI. A total of 5,187 patients (24.1%) were diagnosed with PCa, 2,043 patients (9.5%) were treated with ADT, and 805 patients (3.7%) died from PCa. Pantoprazole was associated with a 3.0% (95% CI 0.3%-6,0%) increased rate of being treated with ADT for every six months of cumulative use, while any use of all other PPIs was associated with a 39.0% (95% CI 18.0%-64.0%) increased PCa-specific mortality. No significant association was found with PCa diagnosis. Conclusions: Upon validation of the potentially negative association of PPIs with PCa outcomes, the expansive use of PPIs may need to be reassessed, especially in PCa patients.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".