Identifying serious underlying diagnoses among patients with brief resolved unexplained events (BRUEs): a Canadian cohort study
Bibliographic record
Abstract
OBJECTIVE: To describe the demographics and clinical outcomes of infants with brief resolved unexplained events (BRUE). DESIGN: A retrospective cohort study. SETTING: 11 centres within the Canadian Paediatric Inpatient Research Network. PATIENTS: Patients presenting to the emergency department (ED) following a BRUE (2017-2021) were eligible, when no clinical cause identified after a thorough history and physical examination. MAIN OUTCOME MEASURES: Serious underlying diagnosis (requiring prompt identification) and event recurrence (within 90 days). RESULTS: Of 1042 eligible patients, 665 were hospitalised (63.8%), with a median stay of 1.73 days. Diagnostic tests were performed on 855 patients (82.1%), and 440 (42.2%) received specialist consultations. In total, 977 patients (93.8%) were categorised as higher risk BRUE per the American Academy of Pediatrics guidelines. Most patients (n=551, 52.9%) lacked an explanatory diagnosis; however, serious underlying diagnoses were identified in 7.6% (n=79). Epilepsy/infantile spasms were the most common serious underlying diagnoses (2.0%, n=21). Gastro-oesophageal reflux was the most common non-serious underlying diagnosis identified in 268 otherwise healthy and thriving infants (25.7%). No instances of invasive bacterial infections, arrhythmias or metabolic disorders were found. Recurrent events were observed in 113 patients (10.8%) during the index visit, and 65 patients had a return to ED visit related to a recurrent event (6.2%). One death occurred within 90 days. CONCLUSIONS: There is a low risk for a serious underlying diagnosis, where the majority of patients remain without a clear explanation. This study provides evidence-based risk for adverse outcomes, critical information to be used when engaging in shared decision-making with caregivers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".