Use of CT for Head Trauma: 2007–2015
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: International efforts have been focused on identifying children at low risk of clinically important traumatic brain injury in whom computed tomography (CT) neuroimaging can be avoided. We sought to determine if CT use for pediatric head trauma has decreased among US emergency departments (EDs). METHODS: This was a cross-sectional analysis of the National Hospital Ambulatory Care Medical Survey database of nationally representative ED visits from 2007 to 2015. We included children <18 years of age evaluated in the ED for head injury. Survey weighting procedures were used to estimate the annual proportion of children who underwent CT neuroimaging and to perform multivariable logistic regression. RESULTS: There were an estimated 14.3 million pediatric head trauma visits during the 9-year study period. Overall, 32% (95% confidence interval [CI]: 29%–35%) of children underwent CT neuroimaging with no significant annual linear trend (P trend = .50). Multivariate analysis similarly revealed no difference by year (adjusted odds ratio [aOR]: 1.02; 95% CI: 0.97–1.07) after adjustment for patient- and ED-level covariates. CT use was associated with age ≥2 years (aOR: 1.51; 95% CI: 1.13–2.01), white race (aOR: 1.43; 95% CI: 1.10–1.86), highest triage acuity (aOR: 8.24 [95% CI: 4.00–16.95]; P < .001), and presentation to a nonteaching (aOR: 1.47; 95% CI: 1.05–2.06) or nonpediatric (aOR: 1.53; 95% CI: 1.05–2.23) hospital. CONCLUSIONS: CT neuroimaging did not decrease from 2007 to 2015. Findings suggest an important need for quality improvement initiatives to decrease CT use among children with head injuries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".