Risk Prediction After a Brief Resolved Unexplained Event
Bibliographic record
Abstract
OBJECTIVES: Only 4% of brief resolved unexplained events (BRUE) are caused by a serious underlying illness. The American Academy of Pediatrics (AAP) guidelines do not distinguish patients who would benefit from further investigation and hospitalization. We aimed to derive and validate a clinical decision rule for predicting the risk of a serious underlying diagnosis or event recurrence. METHODS: We retrospectively identified infants presenting with a BRUE to 15 children's hospitals (2015-2020). We used logistic regression in a split-sample to derive and validate a risk prediction model. RESULTS: Of 3283 eligible patients, 565 (17.2%) had a serious underlying diagnosis (n = 150) or a recurrent event (n = 469). The AAP's higher-risk criteria were met in 91.5% (n = 3005) and predicted a serious diagnosis with 95.3% sensitivity, 8.6% specificity, and an area under the curve of 0.52 (95% confidence interval [CI]: 0.47-0.57). A derived model based on age, previous events, and abnormal medical history demonstrated an area under the curve of 0.64 (95%CI: 0.59-0.70). In contrast to the AAP criteria, patients >60 days were more likely to have a serious underlying diagnosis (odds ratio:1.43, 95%CI: 1.03-1.98, P = .03). CONCLUSIONS: Most infants presenting with a BRUE do not have a serious underlying pathology requiring prompt diagnosis. We derived 2 models to predict the risk of a serious diagnosis and event recurrence. A decision support tool based on this model may aid clinicians and caregivers in the discussion on the benefit of diagnostic testing and hospitalization (https://www.mdcalc.com/calc/10400/brief-resolved-unexplained-events-2.0-brue-2.0-criteria-infants).
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".