Correlation of fecal calprotectin levels with the detection of treatable enteric pathogens in children with severe acute diarrheal disease in Botswana
Bibliographic record
Abstract
Diarrheal disease is a leading cause of death among young children globally. Current guidelines recommend supportive treatment of acute diarrhea and using antimicrobials only with presence of blood in the stool. Select enteric pathogens, including Shigella, commonly cause disease in high-burden settings; targeted treatment of these pathogens could decrease morbidity and mortality. In settings with limited access to microbiological testing, practical diagnostics are needed to differentiate treatable causes of pediatric diarrhea. Evolving evidence suggests fecal calprotectin (fCal) could help differentiate viral and bacterial gastroenteritis. This study describes a post hoc analysis of stool samples prospectively collected from children hospitalized with severe acute diarrheal disease in Botswana. Specimens were characterized using multiplex PCR panels for selected enteropathogens and assayed for fCal. Stool samples from 312 participants were tested. Samples positive for Shigella had significantly higher fCal than samples positive for rotavirus. Stools that were negative for all assayed pathogens had higher fCal values than expected using standard normative values for healthy children in higher-income settings. Given the prevalence of Shigella and rotavirus infections in young children globally, fCal may be a useful aid to identify children with acute diarrhea for whom antimicrobials could provide benefit and potentially reduce growth failure and mortality.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".