Viral Kinetics and Resistance Development in Children Treated with Neuraminidase Inhibitors: The Influenza Resistance Information Study (IRIS)
Bibliographic record
Abstract
BACKGROUND: We studied the effect of age, baseline viral load, vaccination status, antiviral therapy, and emergence of drug resistance on viral shedding in children infected with influenza A or B virus. METHODS: Samples from children (aged ≤13 years) enrolled during the 7 years of the prospective Influenza Resistance Information Study were analyzed using polymerase chain reaction to determine the influenza virus (sub-)type, viral load, and resistance mutations. Disease severity was assessed; clinical symptoms were recorded. The association of age with viral load and viral clearance was examined by determining the area under the curve for viral RNA shedding using logistic regression and Kaplan-Meier analyses. RESULTS: A total of 2131 children infected with influenza (683, A/H1N1pdm09; 825, A/H3N2; 623, influenza B) were investigated. Age did not affect the mean baseline viral load. Children aged 1-5 years had prolonged viral RNA shedding (±1-2 days) compared with older children and up to 1.2-fold higher total viral burden. Besides, in older age (odds ratio [OR], 1.08; confidence interval [CI], 1.05-1.12), prior vaccination status (OR, 1.72; CI, 1.22-2.43) and antiviral treatment (OR, 1.74; CI, 1.43-2.12) increased the rate of viral clearance. Resistance mutations were detected in 49 children infected with influenza A virus (34, A/H1N1pdm09; 15, A/H3N2) treated with oseltamivir, most of whom were aged <5 years (n = 39). CONCLUSIONS: Children aged 1-5 years had a higher total viral burden with prolonged virus shedding and had an increased risk of acquiring resistance mutations following antiviral treatment. CLINICAL TRIALS REGISTRATION: NCT00884117.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".