CHARACTERIZATION AND RISK FACTOR IDENTIFICATION IN CHILDREN WITH SEVERE CROUP
Bibliographic record
Abstract
Abstract BACKGROUND Croup is a self-limiting illness predominantly affecting young children, the most common etiology being Parainfluenza virus types 1 and 2. While the majority of croup remains self-limiting, 1.6–3% of cases are severe requiring acute airway management such as intubation and ventilatory support. While much is known about the diagnosis, management, and clinical course of self-limiting croup, factors contributing to severe cases remain unexplored. OBJECTIVES To characterize the clinical features and risk factors associated with severe croup in children admitted to a tertiary care centre. DESIGN/METHODS A retrospective chart review study of paediatric patients with severe croup presenting at a single tertiary care paediatric institution between 2011 and 2015 was performed. Severe croup was defined as those requiring major airway intervention (i.e. intubation), emergent management in the operating room (i.e. rigid bronchoscopy) or admission to the paediatric intensive care unit. Our findings were compared to previous reported characteristics of children with viral croup and recurrent croup. Multiple univariate regression models were constructed to isolate predictor variables of longer hospital stay and isolate confounders such as known comorbidities and risk factors. RESULTS Sixty-seven croup patients were analyzed, aged 4–170 months (median, 15) of which 76.8% were male. Several presented with risk factors, namely previous croup history (23.2%), intubation history (13%), airway abnormalities (8.7%) and nonspecific reactive airway disease (8.7%), while others exhibited comorbid conditions such as asthma (7.3%), GERD (14.5%) and known drug allergy (5.8%). In our cohort, Parainfluenza virus type 3 (PIV3) was shown to be a predictor for increased length of stay with a univariate regression coefficient of 3.2 (95% CI 0.72–5.7). Known croup risk factors did not contribute to the effect observed with PIV3 (coefficient 3.0, 95% CI 0.4–5.7). Further analysis accounting for comorbidity confounders predicted a coefficient of 2.6 for PIV3 (95% CI 0.01–5.12). Mean length of stay for PIV3 infected children was 7.4 nights. CONCLUSION To our knowledge, this is the first study linking PIV3 with severe croup and predicting longer hospitalizations in this cohort. Further investigation is required to determine optimal management of PIV3-infected croup patients to shorten clinical course and improve patient outcomes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".