Consequence of Bisphosphonate Use on Dental Implant Removal in Osteoporotic Patient: A Nationwide Cohort Study
Bibliographic record
Abstract
INTRODUCTION: This nationwide population-based cohort study aimed to investigate the relationship between bisphosphonate (BP) use and dental implant removal in patients with osteoporosis. METHODS: A total of 389 226 individuals aged ≥ 65 years with osteoporosis who underwent dental implant surgery between 2014 and 2018 were included. Patients were classified into BP and control groups based on their prescription records. Implant removal was identified using the procedural codes from 2019 to 2020. Multivariate logistic regression analysis was performed to examine the association between BP and implant removal. Subgroup analyses evaluated the impact of the BP administration route (oral vs. intravenous), BP type, and cumulative defined daily dose (DDD) on the risk of implant removal. RESULTS: The BP group demonstrated a modestly increased risk of implant removal compared to the control group (adjusted odds ratio [OR]: 1.09; 95% confidence interval [CI]: 1.05-1.15). Participants with periodontitis had a significantly higher risk of implant removal than participants without periodontitis (adjusted OR: 1.87; 95% CI: 1.63-2.15). Among BP users, the subgroup analysis revealed that intravenous BP administration was associated with a lower risk of implant removal than oral administration (adjusted OR: 0.87; 95% CI: 0.80-0.94). In addition, the risk of implant removal increased progressively with higher cumulative DDDs, highlighting the importance of total BP exposure. CONCLUSION: This study underscores the critical role of cumulative BP exposure in the risk of implant removal, which challenges conventional assumptions regarding administration routes. Future research should explore strategies to optimize implant outcomes in patients with osteoporosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".