The Biophysics of Mandibular Fractures: An Evolution toward Understanding
Bibliographic record
Abstract
BACKGROUND: Predicting outcomes based on a variety of fixation techniques remains problematic in the treatment of mandible fractures. There is inherent difficulty in comparing the hundreds of published articles on the subject because of the large number of variables, including injury patterns, assessment techniques, treatment approach, device selection and application, and definition of outcome. METHODS: The authors review the behavior of the human mandible. Behavior of the intact mandible, multiple fracture scenarios, and small and large (single and multiple) plating applications are reviewed. RESULTS: Several misconceptions in the literature are clarified. Factors that will resolve the dichotomy between clinical results and current biomechanical theories are presented such that a more logical biomechanical model may be used to approach fixation of the mandibular fracture being treated. CONCLUSIONS: Current mandibular biomechanics theory must be expanded to reflect the complex nature of the system and to more accurately describe conditions that exist in the physical world. Otherwise, further analysis in advancements in outcome and treatment will be relegated to chance.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".