Predictors of influenza a molecular viral shedding in Hutterite communities
Bibliographic record
Abstract
BACKGROUND: Patterns of influenza molecular viral shedding following influenza infection have been well established; predictors of viral shedding however remain uncertain. OBJECTIVES: We sought to determine factors associated with peak molecular viral load, duration of shedding, and viral area under the curve (AUC) in children and adult Hutterite colony members with laboratory-confirmed influenza. METHODS: A cohort study was conducted in Hutterite colonies in Alberta, Canada. Flocked nasal swabs were collected during three influenza seasons (2007-2008 to 2009-2010) from both symptomatic and asymptomatic individuals infected with influenza. Samples were tested by real-time reverse-transcription polymerase chain reaction for influenza A and influenza B, and the viral load was determined for influenza A-positive samples. RESULTS: For seasonal H1N1, younger age was associated with a larger AUC, female sex was associated with decreased peak viral load and reduced viral shedding duration, while the presence of comorbidity was associated with increased peak viral load. For H3N2, younger age was associated with increased peak viral load and increased AUC. For pandemic H1N1, younger age was associated with increased peak viral load and increased viral AUC, female sex was associated with reduced peak viral load, while inapparent infection was associated with reduced peak viral load, reduced viral shedding duration, and reduced viral AUC. CONCLUSIONS: Patterns of molecular viral shedding vary by age, sex, comorbidity, and the presence of symptoms. Predictor variables vary by influenza A subtype.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".