The Effect of Antiresorptive Drug Holidays on Medication-Related Osteonecrosis of the Jaw: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The objective of this study is to evaluate the effectiveness of discontinuing high-dose antiresorptive (AR) therapy in reducing the risk of medication-related osteonecrosis of the jaw (MRONJ) in patients treated with AR medications and undergoing dentoalveolar surgery or tooth extractions. The review was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. A literature search was conducted using the databases MEDLINE, Embase, Web of Science, Scopus, and Cochrane Central Register of Controlled Trials (CENTRAL) from inception till the 1st of April, 2022. Both observational and interventional studies that evaluated the effect of AR drug holiday in the development of MRONJ in patients receiving AR medications and who require dentoalveolar surgical procedures were included. Trials published as abstracts, case reports, case series, non-systematic reviews, and others were excluded. All findings were reported as odds ratios (ORs) and corresponding 95% confidence intervals (CIs). The Newcastle-Ottawa Quality Assessment Scale was used to evaluate the methodological quality assessment, and the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach was used to evaluate the quality of the evidence. Eight articles (6808 subjects) were included for analysis. Of the participants, 4847 cases (drug holiday group) were compared to 1961 controls (non-drug holiday group). Based on the random effects model, the pooled summary OR was 0.73 (95% CI: 0.51-1.06) for the drug holiday group compared to the non-drug holiday group. In other words, the drug holiday group was not significantly different from the non-drug holiday group in the development of MRONJ following a tooth extraction procedure (p = 0.10). The statistical heterogeneity was low across all studies (I2 = 13%, p = 0.33). Within the limits of the available evidence, our findings revealed that drug holidays with AR will not minimize the risk of MRONJ and thus cannot be advised. It may be possible to arrive at more definitive conclusions from large prospective studies and randomized trials of good quality.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".