QuickVue influenza test for rapid detection of influenza A and B viruses in a pediatric population, Clinical and Diagnostic Laboratory Immunology
Bibliographic record
Abstract
The performance of a lateral-flow immunoassay, the QuickVue Influenza Test, for detection of influenza A and B viruses in comparison with that of cell culture was evaluated by using nasopharyngeal aspirates, in viral transport medium, from children with respiratory tract infections. The sensitivity and specificity were 79.2 and 82.6%, respectively. Influenza virus infections in children have been associated with increased outpatient visits, admissions, and antibiotic pre-scriptions (4, 7). Rapid diagnostic methods would therefore be useful to prevent unnecessary antibiotics and admissions and facilitate early antiviral administration. The usual method for diagnosis of influenza virus is cell culture, a method that has good sensitivity (9) but is not very timely. A new rapid diagnostic kit (QuickVue Influenza Test; Quidel, San Diego, Calif.) using monoclonal antibodies spe-cific for influenza A and B virus antigens is now available for direct detection from nasal swab, wash, and/or aspirate speci-mens. In our pediatric population, nasopharyngeal aspirates (NPA) are routinely collected for the diagnosis of viral respi-ratory tract infections and submitted in viral transport medium (VTM) for direct immunofluorescence assay (DFA) and cell culture. We evaluated the performance of the QuickVue Influenza Test with NPA submitted in VTM and that of the DFA and compared them with that of cell culture. Methods. NPA submitted in VTM were collected from chil-dren with respiratory tract infections seen at the Montreal
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".