Implant outcomes with Antiresorptives
Bibliographic record
Abstract
Purpose: We conducted a systematic review and meta-analysis evaluating dental implant failure and osteonecrosis related to antiresorptive therapy for osteoporosis. Methods: We searched 5 databases between 1946 and January 2022. We included interventional and non-interventional studies reporting rates of dental implant failure or osteonecrosis in those with osteoporosis or osteopenia. Two reviewers independently screened all titles and abstracts, and full-texts. Risk of bias was assessed using the modified Ottawa-Newcastle scale, and the evidence was assessed using the GRADE framework. We adhered to PRISMA 2020 and MOOSE reporting standards. Results: Our search revealed 793 unique citations that underwent title and abstract screening. We included 112 studies for full text screening, 33 underwent data abstraction, and ultimately nine (n=655) were included for the implant failure analysis. Random effects meta-analysis revealed a point estimate suggesting a decrease in relative risk of implant failure in those exposed to antiresorptives (RR 0.82, 95% CI 0.52 – 1.28, p = 0.38, very low certainty). We identified 128 cases of MRONJ in implant recipients. The rate of MRONJ following implantation in those exposed to antiresorptive therapy is 0.40% pooled from 20 cohorts. A single comparative study assessed risk adjusted MRONJ in osteoporotic patients undergoing dental implant placement and found use of bisphosphonates increased osteonecrosis of the jaw by 3 cases per 1000 patients (adjusted HR 4.09, 95% CI 2.75 – 6.09, p<0.001, moderate certainty). Conclusions: The limited evidence does not suggest an association between antiresorptive therapy for osteoporosis and dental implant failure. The certainty of evidence is very low due to serious methodologic concerns. Antiresorptive therapy likely causes MRONJ in osteoporotic patients receiving dental implants with moderate certainty evidence.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".