Specialty Consultations and Diagnostic Testing Accuracy After Brief Resolved Unexplained Events: A Multicenter Observational Study
Bibliographic record
Abstract
OBJECTIVES: Current BRUE guidelines focus on lower-risk infants (approximately 5%), leaving management strategies for the majority undefined. We aimed to evaluate the diagnostic yield and accuracy of tests and subspecialist consultations among all infants with BRUE. METHODS: In this retrospective cohort (2017-2021) across 11 Canadian hospitals, we included 1042 infants with BRUE. Records within 90 days of the index visit were reviewed to ascertain diagnostic testing and any newly identified underlying diagnoses. Diagnostic accuracy was evaluated by comparing test results to diagnoses confirmed or considered probable by care teams. RESULTS: Among 855 patients (82.1%) who underwent testing, 72 (8.4%) received explanatory diagnoses, and 554 (64.8%) had nonsignificant or incidental findings. Complete blood count (50.2%, N = 523) had low sensitivity (26.3%) and specificity (57.5%) for anemia and bacterial infections. Electrocardiograms (55.3%, N = 576) showed a sensitivity of 45.5% and specificity of 73.5%, while electroencephalograms (23.3%, N = 243) showed higher sensitivity (72.7%) and specificity (83.3%). Tests like liver enzymes, ammonia, lactic acid, blood cultures, and pertussis testing identified no diagnoses. Four laboratory tests showed a false positive rate (FPR) exceeding 50%: blood gas (57.6%), inborn errors of metabolism testing (51.7%), electrolytes (51.3%), and bilirubin (52.8%). Consultations were provided to 440 patients (42.2%), identifying explanatory diagnoses in 122 (27.7%) and incidental findings in 70 (15.9%). CONCLUSIONS: Diagnostic testing and consultations are prevalent but rarely yield significant results, often with high FPR. Consequently, the routine application of these diagnostic approaches should be reconsidered in the absence of targeted clinical indications.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".