UTILIZATION OF COMPUTED TOMOGRAPHY FOR PEDIATRIC HEAD TRAUMA FROM 2007–2014 IN THE UNITED STATES: BEFORE AND AFTER PECARN CLINICAL DECISION RULES
Bibliographic record
Abstract
Abstract BACKGROUND The Pediatric Emergency Care Applied Research Network (PECARN) clinical prediction rules identify children at low risk of clinically important traumatic brain injury in whom computed tomography (CT) neuro-imaging can safely be avoided. Since publication in 2009, these rules have been externally validated and are widely used in the Emergency Department (ED) assessment of children with acute head trauma. OBJECTIVES This study sought to determine if the proportion of children receiving CT-imaging in US EDs following head trauma has decreased following the development of PECARN rules. DESIGN/METHODS This study was a cross-sectional study using the National Hospital Ambulatory Care Survey (NHAMCS) database from 2007–2014. NHAMCS collects data on approximately 30,000 nationally representative visits annually to 300 randomly selected U.S EDs. We included all children <18 years old presenting with a chief complaint or discharge diagnosis of head injury. We collected data on patient demographics, reason for ED visit, discharge diagnosis, patient disposition, and use of head CT. Multivariable logistic regression was used to identify characteristics of CT use, with appropriate weighting to account for the survey methodology. The primary outcome was proportion of children receiving a head CT before and after 2009. RESULTS There were 55,253 paediatric visits during the 8-year study period. Among these, 2,783 (5.3% 95%CI 5.0%-5.6%) met inclusion criteria, representing 12,417,725 paediatric head trauma visits. Median patient age was 6 years (IQR 2–13 years), 62% were male, and a majority were evaluated in non-teaching and non-paediatric hospitals (88% and 90%, respectively). Overall, 32% (95%CI 29%-35%) underwent CT neuroimaging. There was no significant difference in CT use after 2009 (31% after vs. 33% before, p=0.41). Multivariate analysis similarly demonstrated no difference after adjustment for patient age, gender, race, insurance provider, paediatric or teaching hospital, admission status and triage acuity (AOR 1.02 after vs. before, 95%CI 0.79–1.32, p=0.85). Factors associated with increased CT use were age ≥2 years (AOR 1.4, 95%CI 1.1–1.9, p=0.02), admission (AOR 5.3, 95%CI 2.2–12.4, p<0.001), highest triage acuity (AOR 7.3, 95%CI 3.5–15.3, p<0.001) and presentation to a non-teaching (AOR 1.5, 95%CI 1.1–2.2, p=0.02) or non-paediatric (AOR 1.5, 95%CI 1.3–2.8, p<0.01) hospital. CONCLUSION The use of CT neuro-imaging did not decrease in the 5-year period following derivation of PECARN rules. Findings suggest an important need for quality improvement initiatives to ensure appropriate CT utilization among head injured children.
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 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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".