HIV-1 protease and reverse transcriptase mutations for drug resistance surveillance
Bibliographic record
Abstract
OBJECTIVES: Monitoring regional levels of transmitted HIV-1 resistance informs treatment guidelines and provides feedback on the success of HIV-1 prevention efforts. Surveillance programs for estimating the frequency of transmitted resistance are being developed in both industrialized and resource-poor countries. However, such programs will not produce comparable estimates unless a standardized list of drug-resistance mutations is used to define transmitted resistance. METHODS: In this paper, we outline considerations for developing a list of drug-resistance mutations for epidemiologic estimates of transmitted resistance. First, the mutations should cause or contribute to drug resistance and should develop in persons receiving antiretroviral therapy. Second, the mutations should not occur as polymorphisms in the absence of therapy. Third, the mutation list should be applicable to all group M subtypes. Fourth, the mutation list should be simple, unambiguous, and parsimonious. RESULTS: Applying these considerations, we developed a list of 31 protease inhibitor-resistance mutations at 14 protease positions, 31 nucleoside reverse transcriptase inhibitor-resistance mutations at 15 reverse transcriptase positions, and 18 non-nucleoside reverse transcriptase inhibitor-resistance mutations at 10 reverse transcriptase positions. CONCLUSIONS: This list, which should be updated regularly using the same or similar criteria, can be used for genotypic surveillance of transmitted HIV-1 drug resistance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".