Predicting Escalated Care in Infants With Bronchiolitis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Early risk stratification of infants with bronchiolitis receiving airway support is critical for focusing appropriate therapies, yet the tools to risk categorize this subpopulation do not exist. Our objective was to identify predictors of "escalated care" in bronchiolitis. We hypothesized there would be a significant association between escalated care and predictors in the emergency department. We subsequently developed a risk score for escalated care. METHODS: We conducted a retrospective cohort study of previously healthy infants aged <12 months with bronchiolitis. Our primary outcome was escalated care (ie, hospitalization with high-flow nasal cannula, noninvasive or invasive ventilation, or intensive care admission). The predictors evaluated were age, prematurity, day of illness, poor feeding, dehydration, apnea, nasal flaring and/or grunting, respiratory rate, oxygen saturation, and retractions. RESULTS: Of 2722 patients, 261 (9.6%) received escalated care. Multivariable predictors of escalated care were oxygen saturation <90% (odds ratio [OR]: 8.9 [95% confidence interval (CI) 5.1-15.7]), nasal flaring and/or grunting (OR: 3.8 [95% CI 2.6-5.4]), apnea (OR: 3.0 [95% CI 1.9-4.8]), retractions (OR: 3.0 [95% CI 1.6-5.7]), age ≤2 months (OR: 2.1 [95% CI 1.5-3.0]), dehydration (OR 2.1 [95% CI 1.4-3.3]), and poor feeding (OR: 1.9 [95% CI 1.3-2.7]). One of 217 (0.5%) infants without predictors received escalated care. The risk score ranged from 0 to 14 points, with the estimated risk of escalated care from 0.46% (0 points) to 96.9% (14 points). The area under the curve was 85%. CONCLUSIONS: We identified variables measured in the emergency department predictive of escalated care in bronchiolitis and derived a risk score to stratify risk of this outcome. This score may be used to aid management and disposition decisions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".