Dental Implant Failure and Medication-Related Osteonecrosis of the Jaw Related to Dental Implants in Patients Taking Antiresorptive Therapy for Osteoporosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: To inform the 2024 International Task Force on Osteonecrosis of the Jaw update, we conducted a systematic review and meta-analysis evaluating dental implant failure and medication-related osteonecrosis of the jaw (MRONJ) related to antiresorptive therapy for osteoporosis. METHODS: We searched 5 databases (1946-2024) for interventional and noninterventional studies reporting rates of dental implant failure or osteonecrosis in those with osteoporosis or osteopenia. Two reviewers independently screened all titles, abstracts, and full texts. Risk of bias was assessed using the modified Ottawa-Newcastle scale, and the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation. RESULTS: We found 793 unique citations. Nine studies (n = 655) were included in the implant failure analysis. Random-effects meta-analysis revealed wide confidence intervals (CIs) for implant failure among those exposed to antiresorptives (relative risk, 0.82; 95% CI, 0.52-1.28; P = .38, very low certainty). Sensitivity analysis at the level of implant suggested that antiresorptives reduce implant failure (relative risk, 0.53; 95% CI, 0.34-0.81; P = .003, very low certainty). We identified 186 cases of MRONJ in implant recipients. The pooled rate of MRONJ following implantation in those exposed to antiresorptive therapy was 0.5% pooled from 21 cohorts. A single report of risk-adjusted MRONJ found that bisphosphonates increased MRONJ by 3 cases per 1000 patients (adjusted hazard ratio, 4.09; 95% CI, 2.75-6.09; P < .001, moderate certainty). CONCLUSIONS: The low-certainty evidence suggests that antiresorptive therapy for osteoporosis reduces dental implant failure. Bisphosphonates are associated with MRONJ in patients with osteoporosis receiving dental implants with moderate certainty.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".