Double-level noncontiguous thoracic Chance fractures treated with percutaneous stabilization: illustrative case
Bibliographic record
Abstract
BACKGROUND: Chance fractures are unstable due to horizontal extension of the injury, disrupting all three columns of the vertebra. Since being first described in 1948, Chance fractures have been commonly found at a single level near the thoracolumbar junction. Noncontiguous double-level Chance fractures that result from a single traumatic event are rarely reported in the literature. OBSERVATIONS: The authors report a case of an 18-year-old male who presented to the emergency department after a rollover motor vehicle accident. The patient complained of severe back pain when at rest and had no neurological deficits. Computed tomography revealed two unstable Chance fractures of bony subtype located at T6 and T11. The patient underwent percutaneous stabilization from T4 to T12. The postoperative assessment revealed continued 5/5 power bilaterally in all extremities, back pain, and the ability to ambulate with a walker. At 3 months after the operation, clinical assessment revealed no significant back pain and the ability to walk independently. Imaging confirmed stable fixation of the spine with no acute osseous or hardware complications. LESSONS: This report complements previous studies demonstrating support for more extensive stabilization for such unique fractures. Additionally, rapid radiological imaging is needed to identify the full injury and lead patients to appropriate treatment.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".