Predicting Future Acute Care Visit Risk in Kids With Asthma (PARKA)
Bibliographic record
Abstract
OBJECTIVES: We aimed to develop a clinical risk score to predict future asthma acute care visits [emergency department (ED) visits or hospitalizations] within 1 year following a discharge from 1 of 2 tertiary care pediatric EDs in Ontario, Canada. METHODS: We assembled a nested Ontario cohort from the multicenter prospective DOORWAY cohort study and included children 1 to 17 years of age, with an ED visit for a moderate/severe asthma exacerbation. We linked this with provincial health administrative data. We used multivariable regression to derive and internally validate a practical clinical risk score to predict future asthma acute care visits. RESULTS: A total of 257 children [32% female, median age 3.0 years (IQR 1 to 7 y)] were included, and 58 experienced an asthma visit within the following year. These were best predicted by 4 factors: food allergy (OR 4.2, 95% CI: 1.2-14.9), family history of asthma (OR 0.5, 95% CI: 0.3-0.9), prior acute asthma medical visits (OR 2.8, 95% CI: 0.9-8.6), and prior emergency room visits for any respiratory diagnosis (OR 3.0, 95% CI: 1.4-6.4). A score of 0, 1, or 2 points was applied to each factor for up to a maximum of 6 points; the PARKA score has very good overall performance with a scaled Brier score of 0.11 on internal validation and good discrimination with an AUC of 0.72 (95% CI: 0.64-0.78). CONCLUSIONS: The PARKA score predicts the risk of a future asthma acute care visit in a cohort of Ontario children with a moderate/severe asthma ED visit. Following external validation, this tool may aid ED clinicians in accurately targeting resource-intensive preventative interventions for at-risk children.
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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".