Injuries significantly associated with thoracic spine fractures: A case–control study
Bibliographic record
Abstract
OBJECTIVE: To determine injuries significantly associated with traumatic thoracic spine (T-spine) fractures METHODS: This was a case-control study undertaken in an adult trauma centre. Cases were patients admitted with a traumatic T-spine fracture between January 1999 and August 2007. Each case had two controls matched for sex, age (+/-5 years) and injury severity classification (major/minor). Data were collected from patient medical records and the trauma service database. Multivariate logistic regression was used to determine injuries significantly associated with T-spine fracture. RESULTS: Two hundred and sixty-one cases and 512 controls were enrolled. In both groups, mean age was 41 years and 70% of patients were male. Univariate analysis revealed a range of injuries that were significantly more common among the cases, especially cervical and lumbar spine injuries, sternal/scapular/clavicular/rib fractures, pneumo/haemothorax and pulmonary contusions (P < 0.01). Skull fractures and lower limb injuries were significantly more common among the controls (P < 0.01). Logistic regression analysis revealed that only cervical and lumbar spine injuries and rib fractures were positively associated with T-spine fracture (P < 0.001). Skull fractures and lower limb injuries were negatively associated with T-spine injury (P < 0.001). CONCLUSION: Cervical and lumbar spine injuries and rib fractures are significantly associated with T-spine fracture. The presence of these injuries should raise suspicion of concomitant T-spine injury.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".