Derivation of the Pediatric Acute Gastroenteritis Risk Score to Predict Moderate‐to‐Severe Acute Gastroenteritis
Bibliographic record
Abstract
OBJECTIVES: Although most acute gastroenteritis (AGE) episodes in children rapidly self-resolve, some children go on to experience more significant and prolonged illness. We sought to develop a prognostic score to identify children at risk of experiencing moderate-to-severe disease after an index emergency department (ED) visit. METHODS: Data were collected from a cohort of children 3 to 48 months of age diagnosed with AGE in 16 North American pediatric EDs. Moderate-to-severe AGE was defined as a Modified Vesikari Scale (MVS) score ≥9 during the 14-day post-ED visit. A clinical prognostic model was derived using multivariable logistic regression and converted into a simple risk score. The model's accuracy was assessed for moderate-to-severe AGE and several secondary outcomes. RESULTS: After their index ED visit, 19% (336/1770) of participants developed moderate-to-severe AGE. Patient age, number of vomiting episodes, dehydration status, prior ED visits, and intravenous rehydration were associated with MVS ≥9 in multivariable regression. Calibration of the prognostic model was strong with a P value of 0.77 by the Hosmer-Lemenshow goodness-of-fit test, and discrimination was moderate with an area under the receiver operator characteristic curve of 0.68 (95% confidence interval [CI] 0.65-0.72). Similarly, the model was shown to have good calibration when fit to the secondary outcomes of subsequent ED revisit, intravenous rehydration, or hospitalization within 72 hours after the index visit. CONCLUSIONS: After external validation, this new risk score may provide clinicians with accurate prognostic insight into the likely disease course of children with AGE, informing disposition decisions, anticipatory guidance, and follow-up care.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".