Bone‐impacted fibular free flap: Long‐term dental implant success and complications compared to traditional fibular free tissue transfer
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to compare complications and dental implant success between the bone-impacted fibula free flap (BIFFF) and the traditional fibular free flap used in mandibular and midface reconstruction. METHODS: Retrospective review of all patients undergoing BIFFF or traditional fibular free flap reconstruction from 2001 to 2009 was undertaken. Complications related to the BIFFF and traditional fibular free flap site of reconstruction were compared. Dental implant success rates for each type of flap were compared at 1-year intervals for 5 years. RESULTS: One hundred fourteen patients underwent 81 BIFFFs and 35 traditional fibular free flaps. No significant difference in complications between BIFFF (20.9%) and traditional fibular free flap (25.7%) reconstruction was observed. Logistic regression analysis revealed only the site as a predictor of both single and multiple complications. At 5 years postimplantation, dental implant success rates were 95.5% and 77.1% for BIFFF and traditional fibular free flap, respectively (p = .006). CONCLUSION: BIFFF reconstruction is a novel surgical technique that may improve long-term dental implant success rates with no additional risk of complications. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1783-E1787, 2016.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".