Biomarkers to predict the onset of biphosphonate-related osteonecrosis of the jaw: A systematic review
Bibliographic record
Abstract
BACKGROUND: The goal of this paper was to identify available biomarkers to predict the onset of biphosphonate-related osteonecrosis of the jaw (BRONJ). MATERIAL AND METHODS: Case-control studies comparing the different concentrations of a series of molecules detected in serum and urine as matrices of BRONJ affected patients vs. non-affected were included. PRISMA guidelines for systematic reviews were used for the present paper. Two reviewers independently screened electronic databases (Medline, Web of science, and The Cochrane Library) and performed hand searches. Risk of bias assessment of selected studies was performed by the Newcastle-Ottawa Scale. This study is registered as PROSPERO CRD42017078149. RESULTS: From a total of 601 identified studies, 7 (4 articles with high methodological quality and 3 with medium) articles were included. They investigate 2623 patients, of whom 91 (3.47%) developed BRONJ. A total of 7 biomarkers were identified and classified into 3 groups: bone turnover, angiogenesis and endocrine markers. Conflicting results were found in relation to most biomarkers. CONCLUSIONS: The present review suggests that no useful markers are currently available to evaluate BRONJ risk. Nevertheless, the present paper indicates that a paradigm shift from bone turnover biomarkers to angiogenesis and endocrine markers could shed light on this search.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".