Stratification of risk for emergent intracranial abnormalities in children with headaches: a Pediatric Emergency Care Applied Research Network (PECARN) study protocol
Bibliographic record
Abstract
INTRODUCTION: Headache is a common chief complaint of children presenting to emergency departments (EDs). Approximately 0.5%-1% will have emergent intracranial abnormalities (EIAs) such as brain tumours or strokes. However, more than one-third undergo emergent neuroimaging in the ED, resulting in a large number of children unnecessarily exposed to radiation. The overuse of neuroimaging in children with headaches in the ED is driven by clinician concern for life-threatening EIAs and lack of clarity regarding which clinical characteristics accurately identify children with EIAs. The study objective is to derive and internally validate a stratification model that accurately identifies the risk of EIA in children with headaches based on clinically sensible and reliable variables. METHODS AND ANALYSIS: Prospective cohort study of 28 000 children with headaches presenting to any of 18 EDs in the Pediatric Emergency Care Applied Research Network (PECARN). We include children aged 2-17 years with a chief complaint of headache. We exclude children with a clear non-intracranial alternative diagnosis, fever, neuroimaging within previous year, neurological or developmental condition such that patient history or physical examination may be unreliable, Glasgow Coma Scale score<14, intoxication, known pregnancy, history of intracranial surgery, known structural abnormality of the brain, pre-existing condition predisposing to an intracranial abnormality or intracranial hypertension, head injury within 14 days or not speaking English or Spanish. Clinicians complete a standardised history and physical examination of all eligible patients. Primary outcome is the presence of an EIA as determined by neuroimaging or clinical follow-up. We will use binary recursive partitioning and multiple regression analyses to create and internally validate the risk stratification model. ETHICS AND DISSEMINATION: Ethics approval was obtained for all participating sites from the University of Utah single Institutional Review Board. A waiver of informed consent was granted for collection of ED data. Verbal consent is obtained for follow-up contact. Results will be disseminated through international conferences, peer-reviewed publications, and open-access materials.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".