Traumatic Spinal Cord Injuries with Fractures in a Québec Level I Trauma Center
Bibliographic record
Abstract
BACKGROUND: Traumatic spinal cord injuries (TSCI) have devastating consequences on patients' quality of life. More specifically, TSCI with spinal fractures (TSCIF) have the most severe neurological impairment, although limited data are available. This study aimed at providing data and analyzing TSCIF in a level I trauma center in the province of Québec, Canada. METHODS: Two hundred eighty-two TSCIF were reviewed. Spinal injuries and neurological impairment were assessed with AO classification and AIS, respectively. Variables included age, sex, cause, location, mechanism of injury (MOI), and severity of TSCIF. Chi-squared Pearson determined significant associations (p < 0.05). RESULTS: Male-to-female ratio was 3.21:1. Patients were 42.5 ± 18.7 years. The leading causes of TSCIF were high-energy falls (28.4%), cars (26.2%) and vehicle without restraint system (motorcycle, all-terrain vehicle, snowmobile, and bicycle) (21.3%). Vehicle collisions, pooling cars and unrestrained vehicles, mostly affected the 20-49-year population (62.2%). The main MOI was distraction in males (47.9%), and axial compression in females (44.8%). There were significant associations between causes and injured spinal level, as well as between MOI and injured spinal level, sex, and TSCIF severity. Most patients involved in unrestrained vehicle accidents sustained a thoracolumbar spine distraction with complete motor deficit. A severe neurologic deficit affected most patients following car accidents that caused cervical spine distraction or axial torsion. CONCLUSIONS: In Québec, most TSCIF caused by vehicle collisions affect a young population and have severe neurological impairments. Future efforts should focus on better understanding accidents involving the unrestrained vehicle category to further improve preventive measures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".