Antiresorptive Therapy to Reduce Fracture Risk and Effects on Dental Implant Outcomes in Patients With Osteoporosis: A Systematic Review and Osteonecrosis of the Jaw Taskforce Consensus Statement
Bibliographic record
Abstract
OBJECTIVE: Placement of a dental implant in a patient on antiresorptive therapy has been hypothesized to increase the risk of medication-related osteonecrosis of the jaw (MRONJ) and/or impact implant survival. In patients with osteoporosis, the risk of MRONJ with antiresorptive therapy is only marginally higher than observed in the general population. METHODS: The International ONJ Taskforce conducted a systematic review of the literature and evaluated the outcomes of implant placement in individuals with osteoporosis receiving antiresorptive therapy. RESULTS: The data were reviewed by the International Taskforce, and consensus was achieved on the following GRADEd recommendation. In patients with osteoporosis on antiresorptive therapy, the Taskforce suggests that antiresorptive therapy does not need to be stopped prior to proceeding with dental implant (weak recommendation, very low-quality evidence). Long-term bisphosphonate use maybe associated with a small increase in the risk of MRONJ (3 cases per 1000 patients; adjusted hazard ratio: 4.09, 95% CI: 2.75-6.09, P < .001, moderate certainty). CONCLUSION: Current evidence does not suggest an association between antiresorptive therapy in patients with osteoporosis and dental implant failure. Implants may be safely placed in the presence of concomitant use of bisphosphonates or denosumab in patients with osteoporosis with no evidence of an increased risk of implant failure/compromise.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".