Clinical Clearance of the Cervical Spine in Blunt Trauma Patients Younger Than 3 Years: A Multi-Center Study of the American Association for the Surgery of Trauma
Bibliographic record
Abstract
BACKGROUND: Cervical spine clearance in the very young child is challenging. Radiographic imaging to diagnose cervical spine injuries (CSI) even in the absence of clinical findings is common, raising concerns about radiation exposure and imaging-related complications. We examined whether simple clinical criteria can be used to safely rule out CSI in patients younger than 3 years. METHODS: The trauma registries from 22 level I or II trauma centers were reviewed for the 10-year period (January 1995 to January 2005). Blunt trauma patients younger than 3 years were identified. The measured outcome was CSI. Independent predictors of CSI were identified by univariate and multivariate analysis. A weighted score was calculated by assigning 1, 2, or 3 points to each independent predictor according to its magnitude of effect. The score was established on two thirds of the population and validated using the remaining one third. RESULTS: Of 12,537 patients younger than 3 years, CSI was identified in 83 patients (0.66%), eight had spinal cord injury. Four independent predictors of CSI were identified: Glasgow Coma Score <14, GCSEYE = 1, motor vehicle crash, and age 2 years or older. A score of <2 had a negative predictive value of 99.93% in ruling out CSI. A total of 8,707 patients (69.5% of all patients) had a score of <2 and were eligible for cervical spine clearance without imaging. There were no missed CSI in this study. CONCLUSIONS: CSI in patients younger than 3 years is uncommon. Four simple clinical predictors can be used in conjunction to the physical examination to substantially reduce the use of radiographic imaging in this patient population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".