Prevalence of Free Flap Failure in Patients Undergoing Reconstruction for Medication-Related Osteonecrosis of the Jaw: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication in patients treated with antiresorptive or antiangiogenic agents, particularly those with cancer-related comorbidities. This systematic review and meta-analysis aimed to estimate the prevalence of free flap failure in patients undergoing microvascular reconstruction for MRONJ. Methods: A comprehensive literature search was conducted across Medline/PubMed, Scopus, and Web of Science up to 30 January 2025. Inclusion criteria were observational studies involving MRONJ patients treated with free flap reconstruction. Risk of bias was assessed using the Newcastle–Ottawa Scale. The pooled prevalence of free flap failure was calculated using a random-effects model with Freeman–Tukey double arcsine transformation. Results: Twelve studies were included in the quantitative analysis. The fibula free flap was the most frequently used flap. The pooled prevalence of free flap failure was 0.1% (95% CI: 0–2.3%), with no significant associations observed in meta-regression analyses for publication year, patient age, or sex. All included studies were of moderate methodological quality. Conclusions: These findings suggest that free flap reconstruction is a reliable and effective surgical option for managing advanced MRONJ in well-resourced and specialized healthcare settings; however, limitations such as small sample sizes and heterogeneity in protocols must be considered. Further high-quality, multicenter studies are needed to evaluate long-term outcomes and refine perioperative management strategies.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".