Prospective Evaluation of Computed Tomographic Scanning for the Spinal Clearance of Obtunded Trauma Patients: Preliminary Results
Bibliographic record
Abstract
BACKGROUND: Screening methods for detecting cervical spine injury in obtunded ventilated patients continue to evolve. This study compared the use of plain radiography to computed tomographic (CT) scanning of cervical spines in the obtunded blunt trauma patient. The accuracy of plain radiography and CT scanning in detecting clinically significant cervical spine injury in the obtunded blunt trauma patient was evaluated. METHODS: We conducted a prospective cohort study with a 3-year convenience sample. The study population consisted of a high-risk subpopulation of severely injured patients, intubated or with a Glasgow Coma Scale score < 9 at presentation. Patients were assessed with a three-view cervical spine series and a CT scan of their cervical spines from the skull base to T1. Independent-blinded review of plain radiographs and CT scans was performed by two radiologists. Sensitivity, specificity, and accuracy of plain films were compared with CT scanning. Sensitivity of CT scanning was compared with discharge diagnosis of cervical spine or cord injury. RESULTS: One hundred two patients were eligible and underwent three-view plain radiography and CT scanning. Sensitivity, specificity, and accuracy of plain films compared with CT scanning were 39%, 98%, and 88%, respectively. CT scanning was 100% sensitive in detecting cervical spine injury. CONCLUSION: CT scanning in conjunction with plain films enhances the number of cervical spine injuries seen radiographically. Application of a protocol of plain radiographs and CT scanning may be used to clear cervical spines in the obtunded trauma patient. Ongoing evaluation of this protocol is required.
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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 | 0.003 | 0.014 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".