Bone impacted fibular free flap: A novel technique to increase bone density for dental implantation in osseous reconstruction
Bibliographic record
Abstract
BACKGROUND: Fibular free flap (FFF) bone has thick cortical bone surrounding a fatty marrow. The cortex has sufficient density for dental implantation, but the marrow limits bone stock. A novel technique was devised to increase bone density: the bone-impacted fibular free flap (BIFFF). The purpose of this study was to: (1) describe the BIFFF technique; (2) evaluate the bone density of BIFFF; and (3) evaluate the stability/success of implants placed in BIFFFs. METHODS: Patients undergoing maxillary/mandibular reconstruction with FFFs were prospectively enrolled from 1998 to 2008. Two cohorts were compared: BIFFF and nonmodified FFF. The main outcome was bone density as seen on CT scans. Primary dental implant stability was determined via Periotest. RESULTS: Thirty-eight patients were included in this study. BIFFFs achieved higher bone density versus unmodified FFFs (p < .05). Greater primary dental implant stability occurred in BIFFFs (p < .05). One hundred percent of BIFFF and 59% of nonmodified FFF implants were successful at 1 year. CONCLUSION: BIFFF increases reconstructed bone density, initial dental implant stability, and 1-year implant success.
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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 | 0.001 |
| 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) | 0.001 | 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".