High Levels of Co-detection of Norovirus and Other Enteric Pathogens in Hospitalized Patients with and without Acute Gastroenteritis in Bangladesh
Bibliographic record
Abstract
Abstract Background The contribution of various enteropathogens to the occurrence of acute gastroenteritis (AGE) remains uncertain in highly endemic settings. We describe the frequency of norovirus-only detections and norovirus co-detections in hospitalized patients in Bangladesh and compare their clinical severity and viral load. Methods From March 2018–October 2021, 1,250 AGE cases and 1,250 non-AGE controls of all ages were enrolled at 10 tertiary care hospitals in Bangladesh. All norovirus-positive AGE cases (n=111) and non-AGE controls (n=182), and a randomly selected subset of 126 norovirus-negative AGE cases, were tested for other enteric viral, bacterial, and parasitic co-pathogens with quantitative real-time PCR assays. We used cycle threshold (Ct)-values as a proxy for viral load and the Vesikari scale to assess disease severity. Results Overall, 92% (218/237) of AGE cases had ≥1 enteropathogen detected. Among 293 norovirus-positive AGE cases and non-AGE controls, 88 (30%) were norovirus-only detections and 205 (70%) were norovirus co-detections. Norovirus-rotavirus was the predominant co-detection, found in 140 (68%) of 205 norovirus co-detections. No differences in clinical severity were observed among AGE cases with norovirus-only versus norovirus co-detections. The median (interquartile range) Ct-values among genogroup II norovirus-only AGE cases, norovirus co-detection AGE cases, and norovirus-positive non-AGE controls were 26 (21–31), 25 (20–29), and 25 (21–30), respectively. Conclusions The frequent co-detection of norovirus with other enteropathogens, especially rotavirus, along with overlapping Ct-values in patients with and without AGE, and between norovirus-only and norovirus co-detections, complicates attributing norovirus as a primary cause of AGE in hospitalized patients in Bangladesh.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".