The Prevalence of Drug Resistance Mutations Among Treatment-Naive HIV-Infected Individuals in Beijing, China
Bibliographic record
Abstract
To investigate the prevalence of HIV-1 genotypic mutations for drug resistance among patients in Beijing, blood samples from 145 newly confirmed (2006-2007), treatment-naive HIV-1-infected individuals were analyzed. Seven subtypes or CRF were subsequently determined and scored by the Stanford HIV Drug Resistance algorithm: CRF01_AE HIV-1 (27.6%), subtype B' (24.1%), CRF07_BC (21.4%), subtype B (20.7%), CRF08_BC (3.4%), subtype C (2.1%), and CRF06_cpx (0.7%). Eleven of the 145 subjects studied were found to harbor the strains resistant to either protease inhibitors (PIs) (3.4%), or nucleoside reverse transcriptase inhibitors (NRTIs) (2.1%), or nonnucleoside reverse transcriptase inhibitors (NNRTIs) (3.4%). Although the prevalence of drug resistance was relatively low among the treatment-naive HIV-1 patients in Beijing in comparison to those in industrialized countries, we will continue monitoring newly infected subjects for any potential alteration of the prevalence pattern to ensure the success of the ongoing scale-up of antiretroviral treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".