Development and Application of Performance Assessment Criteria for Next-Generation Sequencing-Based HIV Drug Resistance Assays
Bibliographic record
Abstract
Next-generation sequencing (NGS)-based HIV drug resistance (HIVDR) assays outperform conventional Sanger sequencing in scalability, sensitivity, and quantitative detection of minority resistance variants. Thus far, HIVDR assays have been applied primarily in research but rarely in clinical settings. One main obstacle is the lack of standardized validation and performance evaluation systems that allow regulatory agencies to benchmark and accredit new assays for clinical use. By revisiting the existing principles for molecular assay validation, here we propose a new validation and performance evaluation system that helps to both qualitatively and quantitatively assess the performance of an NGS-based HIVDR assay. To accomplish this, we constructed a 70-specimen proficiency test panel that includes plasmid mixtures at known ratios, viral RNA from infectious clones, and anonymized clinical specimens. We developed assessment criteria and benchmarks for NGS-based HIVDR assays and used these to assess data from five separate MiSeq runs performed in two experienced HIVDR laboratories. This proposed platform may help to pave the way for the standardization of NGS HIVDR assay validation and performance evaluation strategies for accreditation and quality assurance purposes in both research and clinical settings.
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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 | 0.050 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".