Performance of Rapid Influenza Diagnostic Testing in Outbreak Settings
Bibliographic record
Abstract
Rapid influenza diagnostic tests (RIDTs) may be useful during institutional respiratory disease outbreaks to identify influenza and enable antivirals to be rapidly administered to patients and for the prophylactic treatment of those exposed to the virus but not yet symptomatic. The performance of RIDTs at the outbreak level is not well documented in the literature. This study aimed to evaluate the performance of RIDTs in comparison with that of real-time reverse transcription (rRT)-PCR in the context of institutional respiratory disease outbreaks. This study included outbreak-related respiratory specimens tested for influenza virus at Public Health Ontario Laboratories by both RIDT and rRT-PCR, from 1 September 2010 to 30 April 2013. At the outbreak level, performance testing of RIDTs compared to rRT-PCR for the detection of any influenza virus type demonstrated an overall sensitivity of 76.5%, a specificity of 99.7%, a positive predictive value (PPV) of 99.5%, and a negative predictive value of 85.3%. Because of their high specificity and PPV, even outside of the influenza season, RIDTs can play a role in screening for influenza virus in outbreaks and instituting antiviral therapy in a timely manner when positive. RIDTs can also be useful in remote settings where molecular virology testing is not easily accessible. Suboptimal sensitivity of RIDTs can be addressed by the use of molecular testing.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".