Defining the multiplex probe panel for detecting mutating viruses with high clinical sensitivity
Bibliographic record
Abstract
ABSTRACT Nucleic acid technology has emerged as an important diagnostic for infectious diseases, cancer, cardiovascular diseases, and other diseases. However, mismatches between the probes and targets can lead to misdiagnosis. Here we determine how many mismatches between the probe and target lead to poor clinical performance and respond by developing a rationale multiplex strategy to overcome this detection problem. We found that the probe-target mismatches of greater than 20% yielded clinical sensitivity of 22% or less, rendering the diagnostic test useless. We designed probe panels to improve the clinical sensitivity. We tested our multiplex probe strategy using hepatitis C virus as the model pathogen because this virus has high mutation rates. We showed that we can improve the clinical sensitivity for detecting hepatitis C virus from 31 to 89% when we designed and applied a four-probe panel to the diagnostic test instead of a single probe system. Interestingly, increasing beyond four probes did not significantly increase the clinical sensitivity. We present a strategy to overcome the poor clinical sensitivity of nucleic acid tests for mutating genetic targets. Incorporating this panel design strategy can lead to improved diagnostic test performance, fewer false negatives and more accurate treatment planning for 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".