Sources and Magnitude of Intralaboratory Variability in a Sequence-Based Genotypic Assay for Human Immunodeficiency Virus Type 1 Drug Resistance
Bibliographic record
Abstract
We assessed the intralaboratory reproducibility of a system for sequencing human immunodeficiency virus type 1 (HIV-1) protease (PR) and reverse transcriptase (RT) by using replicate subanalyses of 46 plasma samples collected from HIV-1-infected, antiretroviral-experienced patients in order to determine the relative contributions of the different procedural steps to final sequence variability. Complete sequence concordance between duplicates of each sample was 99.4%. Complete and partial mismatches occurred scattered throughout the PR-RT genome segment at >300 positions. Approximately 75% of the discordances involved mixtures, some of which appeared at key resistance sites. Most differences were the result of the first-round RT-PCR procedure. Inter-rater concordance for sequence analysis and assembly was >99.9%. There was no observed correlation between the number or frequency of mismatches and plasma viral loads. A separate longitudinal analysis of a single routine control sample sequenced 103 times over 9 months consistently gave highly reproducible sequences (median percentage of nucleotide discordances, 0.04%; range, 0 to 0.2%). Finally, sequence data from 168 sequential samples collected from 22 patients with long-term, predominantly wild type HIV showed that intrapatient nucleotide concordance with individual index sequences ranged from 96.5 to 100%. Together, these results confirm that sequence-based genotyping can be a precise and reliable tool for monitoring HIV drug resistance, and they suggest that efforts to reduce variability should focus on the first RT-PCR step. Consequently, the data suggest that the composition of external quality assessment panels should be based on clinical HIV isolates rather than DNA clones.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".