Are We Ready for NGS HIV Drug Resistance Testing? The Second “Winnipeg Consensus” Symposium
Bibliographic record
Abstract
HIV drug resistance is a major global challenge to successful and sustainable antiretroviral therapy. Next-generation sequencing (NGS)-based HIV drug resistance (HIVDR) assays enable more sensitive and quantitative detection of drug-resistance-associated mutations (DRMs) and outperform Sanger sequencing approaches in detecting lower abundance resistance mutations. While NGS is likely to become the new standard for routine HIVDR testing, many technical and knowledge gaps remain to be resolved before its generalized adoption in regular clinical care, public health, and research. Recognizing this, we conceived and launched an international symposium series on NGS HIVDR, to bring together leading experts in the field to address these issues through in-depth discussions and brainstorming. Following the first symposium in 2018 (Winnipeg, MB Canada, 21-22 February, 2018), a second "Winnipeg Consensus" symposium was held in September 2019 in Winnipeg, Canada, and was focused on external quality assurance strategies for NGS HIVDR assays. In this paper, we summarize this second symposium's goals and highlights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".