Mechanism of Injury and Clinical Variables in Thoracic Spine Fracture: A Case Control Study
Bibliographic record
Abstract
Objective To determine the mechanisms of injury and clinical findings significantly associated with traumatic thoracic spine (T-spine) fractures. Methods This was a case-control study in a tertiary adult trauma centre. Cases were patients admitted with traumatic T-spine fractures between January 1999 and August 2007, inclusive. Each case had two controls matched for gender, age and injury severity. Data were collected from patient medical records and the trauma service database. Factors potentially associated with T-spine fracture were derived from the literature, expert consensus and univariate analysis. Multivariate logistic regression was employed to determine factors significantly associated with T-spine fracture. Results Two hundred and sixty one cases and 512 controls were enrolled. Univariate analysis showed the mechanisms of fall from a height ≥2 meters (m) and motorbike accident ≥60 kilometers per hour were significantly associated with T-spine fracture (p<0.001). The clinical findings of thoracic back pain, tenderness, intoxication, step deformity and abnormal neurological symptoms were also significantly associated with T-spine fracture (p<0.05). Multivariate analysis indicated that falls from a height of ≥2 m and thoracic back pain were significantly and positively associated with T-spine fracture (p<0.001). However, intoxication was negatively associated with T-spine fracture. Conclusions Patients with T-spine injury are significantly more likely to have fallen from a height ≥2 m or to have had thoracic back pain but less likely to be intoxicated. These findings should be validated prospectively prior to development of clinical guidelines for the identification of patients who may benefit from CT screening of the thoracic spine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.002 | 0.000 |
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".