Primary drug resistance in antiretroviral-naïve injection drug users
Bibliographic record
Abstract
OBJECTIVES: We evaluated the prevalence of primary HIV drug resistance in a population of 128 injection drug users (48 female) prior to initiating antiretroviral therapy. METHODS: Genotypic and phenotypic profiles were obtained retrospectively for the period June 1996 to February 2007. Genotypic drug resistance was defined as the presence of a major mutation (IAS-USA table, 2007 revision), adding revertants at reverse transcriptase (RT) codon 215. Phenotypic drug resistance was defined as the fold change associated with >or=80% loss of the wild type virologic response due to viral resistance based on virtual phenotype analysis. RESULTS: Genotypic drug resistance was uncommon, and was only identified in six (4.7%) cases, all in the RT gene (L100I, K103N, Y181C, M184V, Y188L, and T215D). There were no cases of multi-class or protease inhibitor (PI) resistance. However, polymorphisms in the protease and RT genes were extremely common. Phenotypic drug resistance was also identified in six (4.7%) patients, four in the RT gene (in patients with mutations K103N, Y181C, M184V and Y188L) and two the protease gene (in two patients with minor PI mutations). In addition, 25 (19.5%) of the patients had reduced susceptibility to PIs, defined as resistance>20% but <80% of the wild type virologic response, with no primary PI mutations detected in all these patients. CONCLUSION: The prevalence of primary HIV drug resistance was low in this population of injection drug users.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".