Dental implant‐based oral rehabilitation in patients reconstructed with free fibula flaps: Clinical study with a <scp>follow‐up</scp> 3 to 6 years
Bibliographic record
Abstract
BACKGROUND: Oral rehabilitation of patients after maxillofacial reconstructive surgery represents a challenge and stable prosthetic retention can be achieved with the use of dental implants. PURPOSE: This retrospective report aimed to evaluate implant-based oral rehabilitation following maxillofacial reconstruction with free fibula flaps. MATERIALS AND METHODS: A total of 14 patients who had reconstruction with fibula flaps either by CAD/CAM or conventional surgery were included in this study. A total of 56 implants (40 in flaps, 16 in native bone) were evaluated. Follow-up after reconstructive surgery ranged between 3.25 and 6.3 years. Follow-up after implant surgery ranged between 1.5 and 3.8 years. RESULTS: Overall survival rate was 85.7% in free fibula flaps and 85.6% in dental implants. Eight implants were lost in three patients and all of these failures were in dental implants inserted in free flaps. According to the results on patient basis, the implant survival was not influenced by any variable. CONCLUSIONS: The maxillofacial reconstruction with free fibula flap and oral rehabilitation with implant-supported prostheses after ablative surgery can be considered as an effective and safe procedure with successful aesthetic and functional outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".