Signal and Noise: Proton Pump Inhibitors and the Risk of Dementia?
Bibliographic record
Abstract
The association between proton pump inhibitor (PPI) use and dementia remains controversial. This cohort study re-examines this issue, addressing shortcomings identified in previous publications using a population-based and a high-dimension propensity-score matched cohort to follow patients for up to 22 years. Cox regression models using baseline characteristics, a lag period, and time-varying variables were used to examine the risk of dementia by cumulative PPI exposure. High-dose PPI users (> 180 days of use) had significantly higher risk of dementia in crude Cox models. After adjustment for medical diagnoses and prescription drug use, these associations disappeared. Among high-dose users starting PPI therapy between 46 and 55 years old, the unadjusted hazard ratio (HR) was 1.55 (95% confidence interval (CI) 1.14, 2.10); the adjusted hazard ratio (aHR) was 1.10 (95% CI 0.80, 1.51). For high-dose users starting therapy between 56 and 65 years, HR = 1.22 (95% CI1.03, 1.44); aHR = 0.99 (95% CI 0.83, 1.17). High-dose users between the ages of 66 and 75 years had no association with the risk of dementia. The use of lag models or time-varying parameters similarly found some association with dementia in crude, but not multivariable Cox models. Although high-dose PPI users were more likely to develop dementia, they were more likely to be diagnosed with dementia risk factors, such as diabetes and cardiovascular disease, which are risk factors for dementia. Controlling for these conditions using multivariable models or a propensity-score matched cohort eliminated this association.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".