Osteonecrosis of the Jaw and Antiresorptive Agents in Benign and Malignant Diseases: A Critical Review Organized by the ECTS
Bibliographic record
Abstract
CONTEXT: Antiresorptive therapy significantly reduces fracture risk in patients with benign bone disease and skeletal-related events (SREs) in patients with bone metastases (BM). Osteonecrosis of the jaw (ONJ) is a rare but severe condition manifested as necrotic bone lesion or lesions of the jaws. ONJ has been linked to the use of potent antiresorptive agents, termed medication-related ONJ (MRONJ). OBJECTIVE: We aimed to identify the differences various aspects of MRONJ among distinct patient categories and provide recommendations on how to mitigate the risk and optimally manage MRONJ in each of them. METHODS: A working group of the European Calcified Tissue Society (ECTS) and 2 experts performed an updated detailed review of existing literature on MRONJ incidence, characteristics, and treatment applied in bone diseases with variable severity of skeletal insult, ranging from osteoporosis to prevention of cancer treatment-induced bone loss and SREs in cancer patients with BM. RESULTS: The risk for MRONJ is much higher in patients with advanced malignancies compared to those with benign bone diseases because of the higher doses and more frequent administration of antiresorptive agents in individuals with compromised general health, along with coadministration of other medications that predispose to MRONJ. The overall risk for MRONJ is considerably lower than the benefits in all categories of patients. CONCLUSION: The risk for MRONJ largely depends on the underlying bone disease and the relevant antiresorptive regimen applied. Physicians and dentists should keep in mind that the benefits of antiresorptive therapy far outweigh the risk for MRONJ development.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".