Evaluating the Impact of Clinical Decision Tools in Pediatric Acute Gastroenteritis: A Population‐based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Acute gastroenteritis (AGE) is a leading cause of pediatric emergency department (ED) visits. Despite evidence-based guidelines, variation in adherence exists. Clinical decision tools can enhance evidence-based care, but little is known about their use and effectiveness in pediatric AGE. This study sought to determine if the following tools-1) pathways/order sets, 2) medical directives for oral rehydration therapy (ORT) or ondansetron, and 3) printed discharge instructions-are associated with AGE admission and ED revisits. METHODS: This was a retrospective population-based cohort study of all children 3 months-18 years with an AGE ED visit in Ontario, Canada, from 2008 to 2010, using linked survey and health administrative databases. Logistic regression models associating clinical decision tools (CDTs) with hospitalizations and revisits controlling for hospital and patient characteristics were employed. RESULTS: Of the 57,921 patient visits during the study period, there were 2,401 hospitalizations (4.2%). A total of 55,520 patients were discharged from the ED, with 2,378 (4.3%) experiencing a 72-hour return visit. In adjusted models, none of the tools were significantly associated with admission. Medical directive for ORT was associated with lower return visit rates (adjusted odds ratio [aOR] = 0.86, 95% confidence interval [CI] = 0.79-0.94] and printed discharge instructions with higher return visits (aOR = 1.33, 95% CI = 1.08-1.65); pathways/order sets and medical directives for ondansetron had no association. CONCLUSIONS: Admissions in children with AGE are not associated with the presence of CDTs. While ORT medical directives are associated with lower ED revisits, printed discharge instructions have the opposite effect. The simple presence/absence of decision support tools does not guarantee improved clinical 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".