Use of a Novel Virus Inactivation Method for a Multicenter Avian Influenza Real-Time Reverse Transcriptase—Polymerase Chain Reaction Proficiency Study
Bibliographic record
Abstract
Proficiency assessments are important elements in quality control for diagnostic laboratories. Traditionally, proficiency testing for polymerase chain reaction (PCR)-based assays has involved the use of clinical samples, samples "spiked" with live agents or DNA plasmids. Because of government regulations and biosecurity concerns, distribution of live high-consequence pathogens of livestock and poultry, such as avian influenza, is not possible, and DNA plasmids are not technically suitable for evaluating RNA virus detection. Therefore, a proficiency testing panel using whole avian influenza in a diluent containing a phenolic disinfectant that inactivates the virus while preserving the RNA for at least 8 weeks at -70 C was developed and used in a multicenter proficiency assessment for a type A influenza real-time reverse transcriptase (RT)-PCR test. The test, which was highly standardized, except for variation in the real-time RT-PCR equipment used, was shown to be highly reproducible by proficiency testing in 12 laboratories in the United States, Canada, and Hong Kong. Variation in cycle threshold values among 35 data sets and 490 samples was minimal (CV = 5.19%), and sample identifications were highly accurate (96.7% correct identifications) regardless of real-time PCR instrumentation.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".