Molecular diagnosis of respiratory virus infections
Bibliographic record
Abstract
The appearance of eight new respiratory viruses, including the SARS coronavirus in 2003 and swine-origin influenza A/H1N1 in 2009, in the human population in the past nine years has tested the ability of virology laboratories to develop diagnostic tests to identify these viruses. Nucleic acid based amplification tests (NATs) for respiratory viruses were first introduced two decades ago and today are utilized for the detection of both conventional and emerging viruses. These tests are more sensitive than other diagnostic approaches, including virus isolation in cell culture, shell vial culture (SVC), antigen detection by direct fluorescent antibody (DFA) staining, and rapid enzyme immunoassay (EIA), and now form the backbone of clinical virology laboratory testing around the world. NATs not only provide fast, accurate and sensitive detection of respiratory viruses in clinical specimens but also have increased our understanding of the epidemiology of both new emerging viruses such as the pandemic H1N1 influenza virus of 2009, and conventional viruses such as the common cold viruses, including rhinovirus and coronavirus. Multiplex polymerase chain reaction (PCR) assays introduced in the last five years detect up to 19 different viruses in a single test. Several multiplex PCR tests are now commercially available and tests are working their way into clinical laboratories. The final chapter in the evolution of respiratory virus diagnostics has been the addition of allelic discrimination and detection of single nucleotide polymorphisms associated with antiviral resistance. These assays are now being multiplexed with primary detection and subtyping assays, especially in the case of influenza virus. These resistance assays, together with viral load assays, will enable clinical laboratories to provide physicians with new and important information for optimal treatment of respiratory virus infections.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".