Dental Implant Placement in Patients With a History of Medications Related to Osteonecrosis of the Jaws: A Systematic Review
Bibliographic record
Abstract
The present systematic review evaluates the safety of placing dental implants in patients with a history of antiresorptive or antiangiogenic drug therapy. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. PubMed, Cochrane Central Register of Controlled Trials, Scopus, Web of Science, and OpenGrey databases were used to search for clinical studies (English only) to July 16, 2019. Study quality was assessed regarding randomization, allocation sequence concealment, blinding, incomplete outcome data, selective outcome reporting, and other biases using a modified Newcastle-Ottawa scale and the Joanna Briggs Institute critical appraisal checklist for case series. A broad search strategy resulted in the identification of 7542 studies. There were 28 studies reporting on bisphosphonates (5 cohort, 6 case control, and 17 case series) and 1 study reporting on denosumab (case series) that met the inclusion criteria and were included in the qualitative synthesis. The quality assessment revealed an overall moderate quality of evidence among the studies. Results demonstrated that patients with a history of bisphosphonate treatment for osteoporosis are not at increased risk of implant failure in terms of osseointegration. However, all patients with a history of bisphosphonate treatment, whether taken orally for osteoporosis or intravenously for malignancy, appear to be at risk of "implant surgery-triggered" medication-related osteonecrosis of the jaw (MRONJ). In contrast, the risk of MRONJ in patients treated with denosumab for osteoporosis was found to be negligible. In conclusion, general and specialist dentists should exercise caution when planning dental implant therapy in patients with a history of bisphosphonate and denosumab drug therapy. Importantly, all patients with a history of bisphosphonates are at risk of MRONJ, necessitating this to be included in the informed consent obtained before implant placement.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".