Nucleic acid amplification-based diagnosis of respiratory virus infections
Bibliographic record
Abstract
The appearance of eight new respiratory viruses in the human population in the past 9 years, including two new pandemics (SARS coronavirus in 2003 and swine-origin influenza A/H1N1 in 2009), has tested the ability of virology laboratories to develop diagnostic tests to identify these viruses. Nucleic acid amplification tests (NATs) that first appeared two decades ago have been developed for both conventional and emerging viruses and now form the backbone of the clinical laboratory. NATs provide fast, accurate and sensitive detection of respiratory viruses and have significantly increased our understanding of the epidemiology of these viruses. Multiplex PCR assays have been introduced recently and several commercial tests are now available. The final chapter in the evolution of respiratory virus diagnostics will be the addition of allelic discrimination and detection of single nucleotide polymorphisms associated with antiviral resistance to multiplex assays. These resistance assays together with new viral load tests will enable clinical laboratories to provide physicians with important information for optimal treatment of patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".