Meta-analysis of the survival rate and postoperative infection rate of primary and secondary implants after vascularized fibula transplantation for reconstruction of jaw defects
Bibliographic record
Abstract
OBJECTIVES: Vascularized fibula flap transplantation is the most effective and common method to repair the jaw defects. In addition, implantation is the first choice to restore dentition on the graft fibula. Implants are usually implanted at least 6 months after fibula transplantation. Primary implantation of implants during surgery can restore the dentition earlier, but whether this method can achieve the same restorative effect as secondary implantation is still uncertain. This article aims to compare the survival rate and complications between primary and secondary implantation through meta-analysis. METHODS: This meta-analysis was conducted according to PRISMA protocol and the Cochrane Handbook of Systematic Reviews of Interventions. According to the inclusion and exclusion criteria, we selected the PubMed, Embase, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), Chinese BioMedical Literature Database (CBM) according to established inclusion and exclusion criteria. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included studies. Meta-analysis was conducted to compare the survival rate and postoperative infection rate of primary and secondary implantation. RESULTS: Seven studies were involved in our research, involving 186 patients. Five of the studies detailed implant success in 106 patients (primary implantation 50, secondary implantation 56), and four studies documented infection after implantation in 117 patients (primary implantation 52, secondary implantation 65); the survival rate of the primary implantation was 93.3%, and the incidence of postoperative infection was 17.3%. The survival rate of the secondary implantation was 93.4%, and 23.1% had postoperative infection. Meta-analysis showed that there was no significant difference in the survival rate between primary implantation and secondary implantation, OR = 0.813 (95% CI 0.383-1.725, P = 0.589 > 0.05), and there was no significant difference in the incidence of postoperative infection, OR = 0.614 (95% CI 0.239-1.581, P = 0.312 > 0.05). CONCLUSIONS: Based on the results of this study, the research found no significant difference in the survival rate or infection rates between primary and secondary implantation. After appropriate indications selection, primary implantation can be used to reconstruct the dentition with less waiting time, reduce the impact of radiotherapy, and bring a higher quality of life for patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".