Multi-analyte suspension arrays for the detection of common viruses: how viable are these assays in clinical laboratories?
Bibliographic record
Abstract
This article assesses the viability of a recently described multi-analyte suspension array for the detection of herpes simplex viruses-1 and -2, cytomegalovirus, Epstein-Barr virus, human papillomavirus and hepatitis B virus. This methodology was identified by the authors as a means of providing rapid, high-throughput multiplex assays that were easy to use. When paired with PCR assays, multi-analyte suspension arrays have the ability to overcome drawbacks associated with conventional detection methods such as long turnaround time, detection sensitivity and the ability to detect only one pathogen in each round of testing. However, the assays described in this article are still hampered by some key issues including limit of detection, the fact that median fluorescence intensity is not truly a quantitative diagnostic method, and that open molecular diagnostic systems can lead to contamination and/or increased operator-based errors. Although modern pressures on clinical virology laboratories have increased the need to develop a system that can detect pathogens in multiplexed assays, in the future these assays will only become more clinically relevant if they are designed with greater stakeholder input.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".