Characterizing the Pain Experience of Children With Acute Gastroenteritis Based on Identified Pathogens
Bibliographic record
Abstract
OBJECTIVES: Pain is common with acute gastroenteritis (AGE) yet little is known about the severity associated with specific enteropathogens. We sought to explore the correlation of pain severity with specific enteropathogens in children with AGE. METHODS: Participants were prospectively recruited by the Alberta Provincial Pediatric EnTeric Infection TEam at 2 pediatric emergency departments (EDs) (December 2014-August 2018). Pain was measured (by child and/or caregiver) using the 11-point Verbal Numerical Rating Scale. RESULTS: We recruited 2686 participants; 46.8% (n = 1256) females, with median age 20.1 months (interquartile range 10.3, 45.3). The mean highest pain scores were 5.5 [standard deviation (SD) 3.0] and 4.2 (SD 2.9) in the 24 hours preceding the ED visit, and in the ED, respectively. Prior to ED visit, the mean highest pain scores with bacterial detection were 6.6 (SD 2.5), compared to 5.5 (SD 2.9) for single virus and 5.5 (SD 3.1) for negative stool tests. In the ED, the mean highest pain scores with bacterial detection were 5.5 (SD 2.7), compared to 4.1 (SD 2.9) for single virus and 4.2 (SD 3.0) for negative stool tests. Using multivariable modeling, factors associated with greater pain severity prior to ED visit included older age, fever, illness duration, number of diarrheal or vomiting episodes in the preceding 24 hours, and respiratory symptoms, but not enteropathogen type. CONCLUSION: Children with AGE experience significant pain, particularly when the episode is associated with the presence of a bacterial enteric pathogen. However, older age and fever appear to influence children's pain experiences more than etiologic pathogens.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".