The relationship among proton pump inhibitors, bone disease and fracture
Bibliographic record
Abstract
INTRODUCTION: There is growing concern about a possible association between the use of proton pump inhibitors (PPIs) and the development of fragility fractures, most notably hip and vertebral fractures. As PPIs are widely used in clinical practice, this association is of paramount clinical importance. AREAS COVERED: The authors review the published papers analyzing the relationship between PPI use and the occurrence of fragility fractures. The authors also explore the data supporting possible mechanisms through which PPIs may increase the risk of fracture, including the effects of PPIs on calcium homeostasis, bone mineral density and direct effects of PPIs on bone metabolism. EXPERT OPINION: Overall, though multiple observational studies have demonstrated an association between PPIs and fragility fractures, the lack of a proven mechanism through which PPIs increase the risk of fracture suggests that this association may not be causal. At this time, the authors do not recommend discontinuing PPIs in patients with a history of fracture or those at increased risk of fracture. However, clinicians should still endeavor to avoid using PPIs in situations where benefits are minimal or clinical indications are lacking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".