Plain Radiography Does Not Add Any Clinically Significant Advantage to Multidetector Row Computed Tomography in Diagnosing Cervical Spine Injuries in Blunt Trauma Patients
Bibliographic record
Abstract
BACKGROUND: Cervical spine (c-spine) injuries (CSI) in trauma patients are common and potentially catastrophic. Numerous guidelines involving clinical and radiologic criteria have been devised to diagnose such injuries. It is not clear whether using plain X-ray films in addition to helical computed tomography (CT) provides any additional benefit in trauma patients who require radiologic clearance of their c-spine. We hypothesized that three standard X-ray views (anteroposterior, lateral, and odontoid) of the c-spine do not provide clinically significant advantage to Multidetector row CT in diagnosing CSI in trauma patients. METHODS: We reviewed the charts of consecutive adult patients with CSI who were admitted to the Trauma Service at a Level I Trauma Center between January 2001 and December 2004. Patients who had CT plus X-ray at admission were entered into the study. Exclusion criteria were age < or = 16 years, incomplete radiology reports, inadequate plain films, or no CSI identified. RESULTS: A total of 121 patients with diagnosed CSI were included in the study. CT picked up 100% of patients who had a CSI diagnosed on plain films and also detected 47 additional CSI that were missed by plain films. The sensitivity for CT was 100%, whereas that of plain films was 61%. Nine patients with CSI (19.1%) who had false-negative plain films required operative intervention. CONCLUSIONS: Three standard X-ray views of the c-spine provided no clinically significant advantage to Multidetector row CT in diagnosing CSI. Revision of current clinical guidelines on c-spine clearance is recommended.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".