The Interrater Reliability of a Validated Bronchiolitis Severity Assessment Tool
Bibliographic record
Abstract
BACKGROUND: We previously constructed and tested a bronchiolitis severity assessment tool in 2 independent hospitals. The model uses age, work of breathing, dehydration and tachycardia to successfully predict disease severity. OBJECTIVE: To prospectively measure the interrater reliability of a bronchiolitis severity assessment tool and of its component variables. DESIGN: Prospective observational survey. SETTING: A county teaching hospital emergency department serving a mixed urban and rural population with an emergency medicine residency program in 2-3-4 format. SUBJECTS: Thirty-two physicians evaluated a convenience sample of children aged less than 18 months presenting to the emergency department with a clinical diagnosis of bronchiolitis during a single season. METHODS: Two physicians independently examined each patient. Each physician completed a physical examination template that included the variables used in the severity assessment tool. Interrater agreement was measured for the variables work of breathing and dehydration and for the tool as a whole using a weighted kappa statistic. RESULTS: One hundred and forty-six cases were enrolled. Twenty-five were dropped for incomplete data collection. The actual weighted agreement on overall classification was 92%; expected, 73%, kappa = 0.676; P < 0.0001. The actual weighted agreement for dehydration was at 95%; expected, 92%, kappa = 0.305; P = 0.0001. The agreement for work of breathing was 95%; expected, 86%; kappa = 0.611; P < 0.0001. The overall model showed better interrater reliability than its individual components. CONCLUSIONS: Overall interrater reliability for this bronchiolitis severity assessment tool is substantial.
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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 | 0.047 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".