Osteonecrosis of the jaws produced by sunitinib: a systematic review
Bibliographic record
Abstract
BACKGROUND: Tyrosine kinase receptor family is involved in tumor growth, pathological angiogenesis and the progression (metastasis) of cancer. Sunitinib (Sutent®) inhibits members of the tyrosine kinase receptor family affecting the induction of angiogenesis and tumor progression. It is not clear if sunitinib increases the risk of osteonecrosis of the jaws (ONJ). The aim of this study was to carry out a systematic review about ONJ related to sunitinib, describing existing cases and possible associated risk factors. MATERIAL AND METHODS: The PubMed/MEDLINE and Cochrane Library databases were searched without date restriction up to September 2018. We included prospective and retrospective observational studies, cross-sectional studies, clinical cases and series of cases, involving only human subjects. The methodological quality of the studies was assessed using The Joanna Briggs Institute (JBI) and Newcastle-Ottawa tools. RESULTS: A total of 13 studies fulfilled our inclusion criteria of which 7 were clinical cases, 5 case series and a retrospective study. All the articles were published between 2009 and 2018. Of the 102 patients treated with sunitinib analyzed in this study, 58 developed ONJ, being or having been treated with sunitinib and bisphosphonates or exclusively with sunitinib. CONCLUSIONS: In this systematic review, we found an increase of ONJ in patients who are medicated with other drugs different than bisphosphonates and denosumab. It is necessary that dentists, oral and maxillofacial surgeons as well as oncologists know the risk of ONJ that these antiresorptive drugs could have. There is a need to continue researching in this field with the aim of an increasing knowledge in this area and creating an adequate protocol of action for this population.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".