Chance-Type Fractures of the Axis
Bibliographic record
Abstract
In Brief Study Design. A retrospective study was performed to identify horizontal fractures of the body of the axis, with special attention to their pattern, prevalence, and clinical outcome. Objective. The prevalence of this type of injury and the long–term clinical behavior are examined. Summary of Background Data. Although isolated cases have been reported, horizontal Chance-type fractures of the body of the axis are not common cervical spine injuries. Methods. The medical records of 674 consecutive patients with fractures of the cervical spine admitted to the authors' institute from 1970 to 2002 were reviewed. Of them, 2 (0.3%) had a horizontal Chance-type fracture of the body of the axis. Neurologic deficits were not diagnosed at admission. Mechanism of injury, treatment, and long-term follow-up were evaluated. Results. Both patients were treated nonoperatively. At the latest follow-up, 3 and 12 years, respectively, both patients had a satisfactory clinical outcome. Conclusion. Horizontal fractures of the Chance-type of the body of the axis are rare. Conservative treatment proved to be effective. Two patients with a horizontal Chance-type fracture of the body of the axis vertebra are presented. Both patients were treated nonoperatively. At 3 and 12 years, respectively, both patients had a satisfactory clinical outcome.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".