Systemic administration of omeprazole interferes with bone healing and implant osseointegration: an in vivo study on rat tibiae
Bibliographic record
Abstract
BACKGROUND: Proton pump inhibitors, over-the-counter drugs taken by millions of patients, diminish bone accrual. Accordingly, we hypothesized that these drugs could impair bone healing and implant osseointegration. This study investigated the effect of post-operative systemic administration of omeprazole on bone healing and implant osseointegration in rat tibiae. METHODS: In 24 Sprague-Dawley rats, a titanium implant was placed in the left tibia, and a bone defect was created in the right tibia. During the 2 weeks following surgery, 12 rats were treated with omeprazole (5 mg/kg, daily) and the other 12 with saline. Then, after euthanasia, the volume (mm(3) ) of the cortical defect and the percentages of newly formed bone in the defect, were assessed using microcomputed tomography; peri-implant bone volume/tissue volume and bone-implant contact percentage were assessed by histomorphometry. RESULTS: Omeprazole-treated rats presented larger cortical defects (2.75 ± 0.59 mm(3) , p = 0.003 versus 2.11 ± 0.36 mm(3) ; p = 0.002) and a lower percentage of newly formed bone in the defects (28.62 ± 13.12; 45.89 ± 9.73; p = 0.003) than controls. Omeprazole-treated rats presented lower peri-implant bone volume/tissue volume (14.3 ± 7.3% versus 30.8 ± 11.0%; p < 0.001) and bone-implant contact (23.3 ± 10.8% versus 41.8 ± 13.3%; p < 0.001) than controls. CONCLUSION: Systemically administered omeprazole impairs bone healing and implant osseointegration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".