Oral care and the use of bone-targeted agents in patients with metastatic cancers: A practical guide for dental surgeons and oncologists
Bibliographic record
Abstract
BACKGROUND: Bone-targeted agents such as bisphosphonates and the RANKL antibody have revolutionised the care of patients with bone metastases. There has, however been increasing concern about the oral health of these patients and in particular osteonecrosis of the jaw (ONJ), especially with the increasing use of these agents at higher potencies for greater periods of time. METHODS: A review of the published data in PubMed and meeting abstracts was performed to examine incidence, risk factors, pathogenesis, clinical course and management of osteonecrosis of the jaw with focus on cancer patients treated with bone-targeted agents (BTA) for bone metastases. This manuscript takes the most frequent and pertinent questions raised by oncologists, dentists and oral and maxillofacial surgeons and tries to give a pragmatic overview of the literature. RESULTS: The incidence of ONJ varies depending on types of bone-targeted agents, duration of treatment and additional risk factors. The causes and pathogenesis of ONJ is not fully elucidated, however bone-targeted therapy induced impaired bone remodelling, microtrauma secondary to jaw activity, and oral bacterial infection seem to be important factors. Since the treatment options for ONJ are limited and not well established, preventive strategies have to be included in patients management. CONCLUSIONS: Many unanswered questions remain about the optimal oral care of patients receiving bone-targeted agents. Prospective data collection will remedy this and help to provide practical guidelines for the management and treatment of those patients that require dental intervention.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".