Epidemiology of Thoracolumbar Spine Injury in Blunt Trauma
Bibliographic record
Abstract
OBJECTIVE: To evaluate the prevalence, distribution, and demographics of thoracolumbar (TL) spine injuries following blunt trauma. METHODS: Prospective, cross-sectional study of a consecutive sample of all blunt trauma patients presenting initially to the emergency department (ED) of a Level 1 trauma center and undergoing thoracic and/or lumbar spine radiography from August 1997 to November 1998. The age, sex, and mechanism of injury of each patient as well as location and type of spine injury were recorded for those patients with vertebral fractures, dislocations, or subluxations. RESULTS: Two thousand four hundred four blunt trauma patients were enrolled. Vertebral injuries were identified in 152 individuals (6.3%, 95% CI = 5.4% to 7.4%). Two hundred sixty distinct anatomic levels of injury were identified in these 152 individuals. Of these 260 injuries, 42 (16.2%) occurred at L1, 38 (14.6%) at L2, 29 (11.1%) at L3, and 27 (10.4%) at T12, making these the most commonly injured vertebrae. Injuries were most common (34 patients) in those aged 30-39 years and were least common (12 patients) in those under 18 years. Compression fractures (52%) were the most common injury in the thoracic spine, while transverse process fractures (48%) were the most common injuries in the lumbar spine. CONCLUSIONS: The prevalence of TL injuries in ED blunt trauma patients undergoing TL radiographs is 6.3%. The most commonly injured area of the TL spine is the thoracolumbar junction.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".