Current status of imaging studies and application of clinical decision rules for pediatric blunt cervical spine injury
Bibliographic record
Abstract
Purpose: We investigated the current status of imaging studies for pediatric blunt cervical spine injury, and applied 3 clinical decision rules to children with blunt trauma of the head or neck in a pediatric emergency center in Korea. The rules included National Emergency X-Radiography Utilization Study (NEXUS) criteria, Canadian Cervical Spine Rule, and Pediatric Emergency Care Applied Research Network risk factors.Methods: This was a retrospective study conducted on 399 children aged 15 years or younger who visited the center after the blunt trauma, and underwent cervical spine radiographs from January 2020 through December 2021. We examined the clinical characteristics per age groups (0-1, 2-5, 6-12, and 13-15 years). Using the 3 rules, we selected children with a potential need for imaging studies (PNI). For this purpose, we analyzed the absence of low-risk variables and the presence of high-risk variables. Predictive performances of the rules were measured for the imaging-confirmed cervical spine injury.Results: The study population (n = 399) had a median age of 5.0 years (interquartile range, 2.0-9.0) and a 64.2% boys’ proportion. Fall (36.6%) was the most common injury mechanism. Two children had the cervical spine injuries. As per NEXUS criteria, Canadian Cervical Spine Rule, and Pediatric Emergency Care Applied Research Network risk factors, 72 (18.0%), 289 (72.4%), and 74 children (18.5%) were classified as those with PNI, respectively. Resultantly, 291 children (72.9%) were classified as having PNI whereas the other 108 (27.1%) were deemed to undergo unnecessary imaging. The 3 rules had nearly 100% sensitivity and negative predictive value, except a 50% sensitivity of NEXUS criteria.Conclusion: Imaging studies can be minimized for children with blunt trauma of the head or neck who are deemed without PNI per the 3 current clinical decision rules. More elaborate criteria are needed to make a timely diagnosis.
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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".