Antihypertensive Medications and the Survival Rate of Osseointegrated Dental Implants: A Cohort Study
Bibliographic record
Abstract
PURPOSE: Antihypertensive drugs in general are beneficial for bone formation and remodeling, and are associated with lower risk of bone fractures. As osseointegration is influenced by bone metabolism, this study aimed to investigate the association between antihypertensive drugs and the survival rate of osseointegrated implants. MATERIALS AND METHODS: This retrospective cohort study included a total of 1,499 dental implants in 728 patients (327 implants in 142 antihypertensive-drugs-users and 1,172 in 586 nonusers). Multilevel mixed effects parametric survival analyses were used to test the association between antihypertensive drugs use and implant failure adjusting for potential confounders. RESULTS: Only 0.6% of the implants failed in patients using antihypertensive drugs while 4.1% failed in nonusers. A higher survival rate of dental implants was observed among users of antihypertensive drugs [HR (95% CI): 0.12 (0.03-0.49)] compared to nonusers. CONCLUSIONS: Our findings suggest that treatment with antihypertensive drugs may be associated with an increased survival rate of osseointegrated implants. To our knowledge, this could be the first study showing that the systemic use of a medication could be associated with higher survival rate of dental implants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".