Interhospital variation in the management of Brief Resolved Unexplained Events (BRUE) in infants: A Canadian multicenter cohort study
Bibliographic record
Abstract
BACKGROUND: Guidelines on Brief Resolved Unexplained Event (BRUE) only provide recommendations for infants categorized at lower risk. However, most infants fall into the higher-risk category, leaving management decisions to individual clinicians and contributing to variation in care. OBJECTIVES: Describe interhospital variation in BRUE management and determine whether higher resource utilization improves detection of serious underlying diagnoses. METHODS: This multicenter observational cohort (2017-2021) included infants (< 12 months) with BRUE at eight Canadian hospitals. We recorded admission, and use of electrocardiograms (ECG), electroencephalograms (EEG), antibiotic and anti-reflux medications, and subspecialty consultations. Multivariable median regression evaluated the association between tests/interventions and length of stay (LOS), and logistic regression assessed whether site-level resource use correlated with serious underlying diagnoses detection. RESULTS: Of 758 infants (92% higher-risk), we noted variation in admission rates (32%-76%, p < .001), ICU admissions (0%-20%, p < .001), median LOS (0.8-2.0 days, p < .001), ECG (24%-78%, p < .001), EEG (8%-29%, p = .001), and anti-reflux medication (0%-21%, p < .001). Five percent had a serious underlying diagnosis, with no significant site differences (0%-8%, p = .49). Median regression showed EEG (19.9 h, 95% CI: 6.8-33.0, p = .03), empiric antibiotics (15.8 h, 95% CI: 4.7-26.9, p = .03), and subspecialty consultation (17.0 h, 95% CI: 10.8-23.2, p < .001) were associated with longer LOS. Higher resource use did not increase detection of serious underlying diagnoses. CONCLUSIONS: Substantial variation exists in BRUE management, associated with prolonged LOS. Higher admission and testing were not associated with increased detection of serious underlying diagnoses. These findings highlight the need for standardized care approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".