A Downward Trend of the Ratio of Influenza RNA Copy Number to Infectious Viral Titer in Hospitalized Influenza A-Infected Patients
Bibliographic record
Abstract
Background. Efficacy endpoints in influenza clinical trials may include clinical symptoms and virological measurements, although virology cannot serve as the primary endpoint. We investigated the relationship between influenza A RNA copy number and quantity of infectious viruses in hospitalized influenza patients. Methods. One hundred fifty influenza-infected, hospitalized patients were included in this prospective cohort study spanning the 2012-2013 influenza season. Daily nasopharyngeal samples were collected during hospitalization, and influenza A RNA copy number and infectious viral titer were monitored. Results. The decay rate for 50% tissue culture infectious dose (TCID50) was 0.51 ± 0.14 log10 TCID50/mL per day, whereas the RNA copy number decreased at a rate of 0.41 ± 0.04 log10 copies/mL per day (n = 433). The log ratio of the RNA copy number to the infectious viral titer within patient changes significantly with -0.25 ± 0.09 units per day (P = .0069). For a 12-day observation period, the decay corresponds to a decline of this ratio of 3 log influenza RNA copies. Conclusions. Influenza RNA copy number in nasal swabs is co-linear with culture, although the rate of decay of cell culture-based viral titers was faster than that observed with molecular methods. The study documented a clear decreasing log ratio of the RNA copy number to the infectious viral titer of the patients over time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".