Detecting and quantifying influenza virus with self‐ versus investigator‐collected mid‐turbinate nasal swabs
Bibliographic record
Abstract
We compared pairs of self‐ and investigator‐collected mid‐turbinate nasal swabs to detect and quantify influenza viral loads. We used RNase P, which reflects presence of human cells to determine adequate sample collection. Sixteen pairs of influenza‐positive swabs and 25 pairs of influenza‐negative swabs were included in this study. The median influenza A viral loads for self‐ and investigator‐collected swabs were 1.68 and 1.67 log 10 copies/mL, respectively ( P = 0.96). RNase P loads were also similar between self‐ and investigator‐collected swabs ( P = 0.51). Self‐collected mid‐turbinate nasal swabs yield comparable viral loads to investigator‐collected swabs, and therefore might be considered for research and clinical management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".