Anatomical examination of the fibula: Digital imaging study for osseointegrated implant installation
Bibliographic record
Abstract
BACKGROUND: Free vascularized fibular flaps are commonly used in jaw reconstruction. CT scan images of the fibula are used in digital planning of jaw reconstruction. In order to fully describe the anatomy of the fibula, an imaging study of the fibula was undertaken. The purpose of the present study was to examine the anatomical structure of the fibula using patient CT images. METHODS: The CT scan images of fibulae of 20 patients were used for the study. The results of the analysis showed that, of the widths, the anterior border of the fibula to the posterior surface was the largest dimension. The shape type analysis showed that the triangular type was most prominent near the head of the fibula, and the irregular type was most prominent towards the lateral malleolus. RESULTS: The results of height and width related to the long axis of implant installation showed that the width of the central section was the largest. With respect to the length of available bone volume, the length near the lateral malleolus was larger than that near the head of the fibula. The results showed that there were significant differences in size between male and female fibulae. CONCLUSION: The present study provides a CT scan based analysis of the anatomy of the fibula. Important information for the optimal site of installation of osseointegrated implants in fibular free flap reconstructions is also provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".