Performance of Stool-testing Recommendations for Acute Gastroenteritis When Used to Identify Children With 9 Potential Bacterial Enteropathogens
Bibliographic record
Abstract
BACKGROUND: The ability to identify bacterial pathogens that necessitate specific clinical management or public health action in children with acute gastroenteritis is crucial to patient care and public health. However, existing stool-testing guidelines offer inconsistent recommendations, and their performance characteristics are unknown. We evaluated 6 leading gastroenteritis guidelines (eg, those of the Centers for Disease Control and Prevention and Infectious Disease Society of America) that recommend when to test children's stool for bacterial enteropathogens. METHODS: Via 2 emergency departments in Alberta, Canada, we enrolled 2447 children <18 years old who presented with ≥3 episodes of diarrhea and/or vomiting in a 24-hour period. All participants were tested for 9 bacterial enteropathogens: Aeromonas, Campylobacter, Escherichia coli O157, other Shiga toxin-producing E. coli, enterotoxigenic E. coli, Salmonella, Shigella, Vibrio, and Yersinia. Patient data gathered at the index visit were used to determine whether guidelines would recommend testing. Sensitivity and specificity to recommend testing for children with bacterial enteropathogens were calculated for each guideline. RESULTS: Outcome data were available for 2391 (97.7%) participants, and 6% (144/2391) of participants tested positive for a bacterial enteropathogen. Guideline sensitivity ranged from 25.8% (95% confidence interval [CI] 18.7-33.0%) to 66.9% (95% CI 59.3-74.6%), and varied for individual pathogens. Guideline specificity for all bacterial enteropathogens ranged from 63.6% (95% CI 61.6-65.6%) to 96.5% (95% CI 95.7-97.2%). CONCLUSIONS: No guideline provided optimally balanced performance. The most sensitive guidelines missed one-third of cases and would drastically increase testing volumes. The most specific guidelines missed almost 75% of cases.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".