Comparison of Cervical Spine Injury Clinical Prediction Rules for Children After Blunt Trauma
Bibliographic record
Abstract
Importance: Pediatric cervical spine injury (CSI) is a rare occurrence; however, CSI can result in significant disability and death. It is essential to determine the optimal CSI clinical prediction rule to risk stratify children with potential CSI after blunt trauma who require cervical spine imaging. Objective: To compare the test characteristics and projected imaging rates between 3 prospectively derived CSI clinical prediction rules: the Pediatric Emergency Care Applied Research Network CSI prediction rule (PECARN CSI rule), the National Emergency X-Radiography Utilization Study (NEXUS), and the Canadian Cervical Spine (c-spine) rule (CCR). Design, Setting, and Participants: This comparative effectiveness study was a planned secondary analysis of a prospective observational study enrolling from December 2018 to October 2021 in 18 PECARN emergency departments. Eligible participants were children up through age 17 years presenting after blunt trauma. Data were analyzed between March 2024 and January 2025. Exposures: Enrollment in the primary study to develop and validate the PECARN CSI prediction rule. Main Outcome Measures: Test characteristics with 95% CIs (sensitivity, specificity, positive predictive value [PPV], negative predictive value [NPV]) and area under the curve (AUC) for the receiver operator curves (ROC) for the detection of CSI using each of the 3 rules. We also estimated the projected c-spine imaging rate (radiography or computed tomography [CT]) based on criteria for each of the 3 rules. Results: There were 22 430 eligible children enrolled (median [IQR] age 8 [2.0-13.0] years; 13 068 male [58.3%]) and 433 (1.9%) had CSI. C-spine imaging was performed 12 768 children (56.9%): 8912 (39.7%) had radiography and 3856 (17.2%) were imaged with CT. The sensitivity of the 3 rules was: PECARN CSI rule, 93.3% (95% CI, 90.9%-95.7%); NEXUS, 85.7% (95% CI, 82.4%-89.0%); and CCR, 90.8% (95% CI, 88.0%-93.5%). The NPV of the 3 rules was: PECARN, 99.8% (95% CI, 99.7%-99.9%); NEXUS, 99.6% (95% CI, 99.5%-99.7%); and CCR, 99.7% (95% CI, 99.6%-99.8%). Strictly applying each rule resulted in projected CT imaging of 1549 children (6.9%) for PECARN, 2419 (10.8%) for NEXUS, and 2968 (13.2%) for CCR. Conclusions and Relevance: In this comparative effectiveness study of CSI prediction rules in children, the PECARN CSI rule had the highest sensitivity and NPV for identifying children at risk for CSI after blunt trauma, with the lowest projected CT imaging rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".