Field Performance of a Rapid Diagnostic Test for Influenza in an Ambulatory Setting
Bibliographic record
Abstract
Provided test characteristics are adequate, point-of-care rapid antigen detection tests for influenza could improve the timeliness and appropriateness of clinical decisions. Our objective was to estimate the field sensitivity and specificity of the Quidel QuickVue Influenza A+B test in an ambulatory setting. The sensitivity and specificity of the Quidel QuickVue test was evaluated against reverse-transcriptase PCR (RT-PCR) on nasopharyngeal specimens collected over two consecutive influenza seasons from ambulatory patients consulting for influenza-like illness (ILI) within 7 days of ILI onset. A total of 491 patients with ILI (180 in 2006 to 2007 and 311 in 2007 to 2008) provided specimens that were tested both by PCR and by the Quidel QuickVue test. Among the 267 patients positive by PCR (55%), 52 were also positive by the QuickVue test, for an overall sensitivity of 19.5% (95% confidence interval [95% CI], 14.7% to 24.2%). Among the 221 PCR-negative patients, 2 were positive for influenza B virus by the rapid test (<1%), for an overall specificity of 99.1% (95% CI, 97.9 to 100%). The field sensitivity of the test varied little with the age or gender of the patient, immunization status, delay since the onset of symptoms, or influenza season. The sensitivity of the test was slightly but nonsignificantly higher for influenza B virus (23%) than for influenza A virus (18%). Despite its high specificity, the low sensitivity of the Quidel QuickVue Influenza A+B test is too poor to direct clinical decisions for ambulatory patients with ILI. Negative results cannot rule out the diagnosis of influenza, and in that context, this test is of questionable utility for routine application in the clinical setting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".