Clinically Significant Neuroimaging Findings Among Pediatric Patients Presenting to the Emergency Department With Symptoms of Psychosis: A Multicenter Retrospective Study
Bibliographic record
Abstract
BACKGROUND: The clinical utility of diagnostic neuroimaging for pediatric patients presenting to the emergency department (ED) for psychosis remains unclear. We sought to estimate the prevalence of and characteristics associated with clinically significant neuroimaging findings among pediatric patients presenting to the ED with symptoms of psychosis who had neuroimaging performed. METHODS: This retrospective cross-sectional study included visits by patients 5 to < 18 years old presenting with symptoms of psychosis to 28 EDs affiliated with the Pediatric Emergency Medicine Collaborative Research Committee from 2016 to 2019 and had neuroimaging performed. We estimated the rate of clinically significant neuroimaging findings, defined as those resulting in further testing, treatment, or medical admission, overall and by imaging modality. Multivariable logistic regression models examined presenting features associated with clinically significant findings. RESULTS: Clinically significant neuroimaging findings were identified in 5.4% (95% CI 4.2%, 6.9%) of 1118 ED visits (54% male, median [IQR] 14 [11-16] years old). Clinically significant findings occurred in 4.9% (34/699) of head computed tomography scans and 7.5% (45/604) of brain magnetic resonance imaging studies (p = 0.07). In a model that imputed missing data, no presenting features were associated with clinically significant neuroimaging findings. In a model that treated missing documentation as absence of the clinical feature, the adjusted odds of clinically significant neuroimaging findings were lower among ED visits by patients with suspected alcohol or substance use (aOR 0.38, 95% CI 0.16, 0.87). CONCLUSION: Among pediatric patients presenting to the ED with symptoms of psychosis who had neuroimaging obtained, approximately 1 in 20 had clinically significant findings. Suspected alcohol or substance use was associated with lower odds of clinically significant neuroimaging findings, although this finding was not consistent across modeling approaches. Prospective studies are needed to definitively evaluate the utility of neuroimaging among children and adolescents presenting to the ED with symptoms of psychosis.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".