Bone That Best Matches the Properties of the Mandible
Bibliographic record
Abstract
OBJECTIVE: The main advantage of primary oromandibular reconstruction using free vascularized bone-containing flaps is improved oral function from (1) the maintenance of mandibular and soft tissue architecture and (2) dental rehabilitation through osseointegrated implants. Bone dimensions, volume, quality, and the ability of the bone to withstand masticatory forces are important factors in achieving successful osseointegration. Previous studies have objectively examined the dimensions of bones used in oromandibular reconstruction, but few have addressed their biomechanical properties. The purpose of this study was to compare the dimensional and biomechanical properties of the bones commonly used in oromandibular reconstruction with those of the mandible. METHODS: Eleven formalin-fixed cadavers were used. The mandibles, fibulae, iliac crests, scapulae, clavicles, second metatarsals, radii, and anterior ribs were harvested. Measurements of the dimensions of the bones were made with calipers at multiple sites. Three-point break strength and screw pulling force tests were then performed on all of the bones. RESULTS: The fibulae, iliac crests, and clavicles had dimensions that best matched those of the mandible. The three-point break strength and screw pulling force tests were consistently the highest for the mandibles and fibulae, followed by the clavicles, scapulae, iliac crests, metatarsals, radii, and ribs, in that order. CONCLUSIONS: The fibula is the bone that best matches the properties of the mandible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".