Outcomes and influential factors in functional and dental rehabilitation following microvascular fibula flap reconstruction in the maxillomandibular region: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background This systematic review and meta-analysis aimed to evaluate the factors influencing and success rates of dental implants for functional and dental rehabilitation following microvascular fibula flap reconstruction in the maxillomandibular region. Main text We conducted a comprehensive search of electronic databases, including MEDLINE, Web of Science, Embase, Scopus, and Cochrane’s CENTRAL, as well as gray literature sources and manual searches of notable journals. The search was performed from inception until February 2023. Studies were included if they examined functional and dental rehabilitation outcomes in patients receiving maxillofacial reconstruction using microvascular fibula flaps and were retrospective or prospective cohort studies involving human subjects. Case–control studies, research involving other reconstruction methods, and animal-based studies were excluded. Data was extracted and confirmed by two independent researchers, and risk of bias was assessed using the Newcastle–Ottawa Scale. Meta-analyses were conducted for dental implant and graft success rate, with separate analyses for different factors affecting the outcome. Heterogeneity was evaluated using Cochran’s Q test and the I2 test. The pooled success rate for implants was 92% and for grafts, 95%, with significant heterogeneity. Implants in fibular grafts had a 2.91 times higher failure rate than those in natural bones. Radiated bone and smoking were identified as factors influencing implant failure, with radiated bone having a 2.29 times higher risk and smokers having a 3.16 times higher risk compared to their respective counterparts. Patient-reported outcomes showed improvements in key areas such as dietary intake, mastication, speech, and esthetics. The success rates declined over time, emphasizing the importance of long-term follow-up. Conclusions Dental implants in free fibula grafts generally have favorable success rates, with minimal bone resorption, manageable probing depths, and limited bleeding on probing. Implant success is influenced by factors such as smoking and radiated bone.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".