Is Mandibular Reconstruction Using Vascularized Fibula Flaps and Dental Implants a Reasonable Treatment?
Bibliographic record
Abstract
PURPOSE: this study retrospectively analyzed the rate of screwed implant insertion and risk factors in patients undergoing mandibular reconstruction with microsurgical revascularized fibula flaps. METHODS: This study retrospectively analyzed all patients with microvascularized fibula grafts between 1997 and 2005. Collected data included general data and risk factors (e.g., smoking, alcohol use), and irradiation was the main predictor variable. The number of patients rehabilitated with dental implants and the implant success rate were evaluated, possible influencing factors were identified, and the results were compared with previously published data. RESULTS: The sample included 33 patients (17 men, 16 women; mean age: 52 years); 76% were smokers, 42% drank alcohol regularly, and 73% had undergone mandible irradiation. Twenty-three patients received 140 screw-retained implants for dental rehabilitation. Twenty-three implants were lost. Overall 1- and 5-year implant survival rates were 94% and 83%, respectively. Implant survival rates were 86% in non-irradiated mandibular bone, 86% in non-irradiated grafted fibular bone, 82% in irradiated mandibular bone, and 38% in irradiated grafted fibular bone. CONCLUSION: This study showed that the use of dental implants in patients with fibula flaps is an appropriate and successful option for dental rehabilitation, even in those with risk factors such as smoking, alcohol use, and irradiation. Implant placement in irradiated grafted bone seems to be a high-risk procedure.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".