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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".