Medication-Related Osteonecrosis of the Jaw: MASCC/ISOO/ASCO Clinical Practice Guideline
Bibliographic record
Abstract
PURPOSE: To provide guidance regarding best practices in the prevention and management of medication-related osteonecrosis of the jaw (MRONJ) in patients with cancer. METHODS: Multinational Association of Supportive Care in Cancer/International Society of Oral Oncology (MASCC/ISOO) and ASCO convened a multidisciplinary Expert Panel to evaluate the evidence and formulate recommendations. Guideline development involved a systematic review of the literature and a formal consensus process. PubMed and EMBASE were searched for studies of the prevention and management of MRONJ related to bone-modifying agents (BMAs) for oncologic indications published between January 2009 and December 2017. Results from an earlier systematic review (2003 to 2008) were also included. RESULTS: The systematic review identified 132 publications, only 10 of which were randomized controlled trials. Recommendations underwent two rounds of consensus voting. RECOMMENDATIONS: Currently, MRONJ is defined by (1) current or previous treatment with a BMA or angiogenic inhibitor, (2) exposed bone or bone that can be probed through an intraoral or extraoral fistula in the maxillofacial region and that has persisted for longer than 8 weeks, and (3) no history of radiation therapy to the jaws or metastatic disease to the jaws. In patients who initiate a BMA, preventive care includes comprehensive dental assessments, discussion of modifiable risk factors, and avoidance of elective dentoalveolar surgery (ie, surgery that involves the teeth or contiguous alveolar bone) during BMA treatment. It remains uncertain whether BMAs should be discontinued before dentoalveolar surgery. Staging of MRONJ should be performed by a clinician with experience in the management of MRONJ. Conservative measures comprise the initial approach to MRONJ treatment. Ongoing collaboration among the dentist, dental specialist, and oncologist is essential to optimal patient care.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".