Community‐acquired respiratory viruses and co‐infection among patients of Ontario sentinel practices, April 2009 to February 2010
Bibliographic record
Abstract
BACKGROUND: Respiratory viruses are known to cocirculate but this has not been described in detail during an influenza pandemic. OBJECTIVES: To describe respiratory viruses, including co-infection and associated attributes such as age, sex or comorbidity, in patients presenting with influenza-like illness to a community sentinel network, during the pandemic A(H1N1)pdm09 in Ontario, Canada. METHODS: Respiratory samples and epidemiologic details were collected from 1018 patients with influenza-like illness as part of respiratory virus surveillance and a multiprovincial case-control study of influenza vaccine effectiveness. RESULTS: At least one virus was detected in 668 (65·6%) of 1018 samples; 512 (50·3%) had single infections and 156 (15·3%) co-infections. Of single infections, the most common viruses were influenza A in 304 (59·4%) samples of which 275 (90·5%) were influenza A(H1N1)pdm09, and enterovirus/rhinovirus in 149 (29·1%) samples. The most common co-infections were influenza A and respiratory syncytial virus B, and influenza A and enterovirus/rhinovirus. In multinomial logistic regression analyses adjusted for age, sex, comorbidity, and timeliness of sample collection, single infection was less often detected in the elderly and co-infection more often in patients <30 years of age. Co-infection, but not single infection, was more likely detected in patients who had a sample collected within 2 days of symptom onset as compared to 3-7 days. CONCLUSIONS: Respiratory viral co-infections are commonly detected when using molecular techniques. Early sample collection increases likelihood of detection of co-infection. Further studies are needed to better understand the clinical significance of viral co-infection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".