64-Slice CT compared to MRI to clear cervical spine injury in high-risk GCS < 14 blunt trauma patients admitted to the ICU
Bibliographic record
Abstract
Objective Clearance of cervical spine injury including ligamentous injury is of paramount importance as results of missed injury may have serious consequences. In obtunded patients, cervical spine clearance is challenging. This study sought to determine whether a negative 64-slice CT scan alone is sufficient to clear cervical spine injury. Patients and methods All consecutive blunt trauma patients admitted to a regional (level 1) trauma center from 1 April 2008 to 31 March 2012 were screened for inclusion in this study. High-risk, GCS < 14, blunt trauma patients were included if they were admitted to the intensive care unit, had a negative 64-slice CT, and MRI of diagnostic quality. GCS was measured at the time of CT scan. Patients with a positive finding on CT scan were excluded. All images were re-interpreted by a trauma radiologist blinded to clinical outcome. Details of missed injuries and clinical impact were reported. The primary outcome was missed clinically significant injury, defined as any injury requiring an additional intervention including continued immobilization or surgery. Results There were 5891 blunt trauma patients admitted to the ICU, 44 of whom met inclusion criteria. Patients had a median injury severity score of 35 and MRI three (2–9) days after CT. Eight of 44 (18%) patients had a positive finding on MRI and five of the findings were clinically insignificant. Three patients (7%) with focal neurologic findings on clinical exam had missed injuries requiring immobilization with a collar. Two of these patients had spine disease, which may have increased their injury risk. Conclusions In high-risk obtunded blunt trauma patients admitted to the ICU, a negative 64-slice CT scan alone is insufficient to clear clinically significant cervical spine injury, with a missed clinically significant injury rate of 7%. When considered with symmetric motor function, a negative 64-slice CT scan may be sufficient. A prospective study is required to confirm these findings.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".