The Relationship Between Proton Pump Inhibitor Use and Longitudinal Change in Bone Mineral Density: A Population-Based From the Canadian Multicentre Osteoporosis Study (CaMos)
Bibliographic record
Abstract
OBJECTIVES: Proton pump inhibitor (PPI) use has been identified as a risk factor for hip and vertebral fractures. Evidence supporting a relationship between PPI use and osteoporosis remains scant. Demonstrating that PPIs are associated with accelerated bone mineral density (BMD) loss would provide supportive evidence for a mechanism through which PPIs could increase fracture risk. METHODS: We used the Canadian Multicentre Osteoporosis Study data set, which enrolled a population-based sample of Canadians who underwent BMD testing of the femoral neck, total hip, and lumbar spine (L1-L4) at baseline, and then again at 5 and 10 years. Participants also reported drug use and exposure to risk factors for osteoporosis and fracture. Multivariate linear regression was used to determine the independent association of PPI exposure and baseline BMD, and on change in BMD at 5 and 10 years. RESULTS: In all, 8,340 subjects were included in the baseline analysis, with 4,512 (55%) undergoing year 10 BMD testing. After adjusting for potential confounders, PPI use was associated with significantly lower baseline BMD at the femoral neck and total hip. PPI use was not associated with a significant acceleration in covariate-adjusted BMD loss at any measurement site after 5 and 10 years of follow-up. CONCLUSIONS: PPI users had lower BMD at baseline than PPI non-users, but PPI use over 10 years did not appear to be associated with accelerated BMD loss. The reasons for discordant findings between PPI use at baseline and during follow-up require further study.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".