Prevalence and molecular characterizations of enterovirus D68 among children with acute respiratory infection in China between 2012 and 2014
Bibliographic record
Abstract
EV-D68 is associated with respiratory tract infections (RTIs). Since its first isolation, EV-D68 has been detected sporadically. However, the US and Canada have experienced outbreaks of EV-D68 infections between August and December 2014. This study aimed to investigate the molecular epidemiology and clinical characteristics of EV-D68 in Chongqing, Southwestern China. From January 2012 to November 2014, 1876 nasopharyngeal aspirate specimens (NPAs) were collected from hospitalized children with RTIs. Among the 1876 NPAs, EV-D68 was detected in 19 samples (1.0%, 19/1876). Of these, 13 samples were detected in September and October 2014 (9.8%, 13/132). Phylogenetic analysis showed that all 13 strains detected in the 2014 Chongqing had high homology with the main strains of the 2014 US outbreak. Among the children with EV-D68 infection, 13 (68%) had a history of recurrent wheezing. A total of 13 children had a discharge diagnosis of asthma. Of these, 11 children were diagnosed with acute asthma exacerbation. EV-D68 was the predominant pathogen that evoked asthma exacerbation in September and October 2014. In conclusion, our results found that a history of recurrent wheezing may be a risk factor for the detection of EV-D68 and viral-induced asthma exacerbation may be a clinical feature of EV-D68 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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".