Bibliographic record
Abstract
The mandible provides structural support for the teeth and a route for neural and vascular supply to the dentition, as well as sensory perception for the lower one-third of the face. Although the mandible is the largest and strongest facial bone, fractures frequently result as a sequela of facial trauma because of the mandible’s physical prominence in the lower face. Vehicular accidents and assaults are the leading causes of mandibular fractures. 1‐3 Open reduction and internal fixation are often the treatment of choice for significantly displaced mandible fractures. 4 Yet reduction of these displaced bony fragments, and their subsequent stabilization during plating, can be difficult and occasionally cumbersome, involving several instruments and multiple hands in a small operative field. Thus, reduction and plating techniques for open repair of mandible fractures often require two surgeons or at least one surgeon and a skilled technician. To lessen the technical assistance needed and to assist with reduction prior to plating, several methods have been proposed in the literature. These involve the use of towel clamps, screws, and wires. 5 However, in theory, these are not completely optimal. With towel clamp and modified towel clamp techniques, the surgeon is still required to manipulate large clamps while drilling and plating. 5 One must also remember that a large clamp remains in the operative field, possibly compromising direct visualization, especially when operating through a small incision. The screw-wire osteosynthesis technique described by Dym and coworkers was used on more than 40 unfavourable mandible angle fractures as the sole source of repair. 6 This technique uses 8-mm screws and 24-gauge wire at the angle of the mandible. Dym and colleagues reported no complications for their small cohort. 6
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".