Antiretroviral therapy resistance mutations among HIV infected people in Kazakhstan
Bibliographic record
Abstract
In Kazakhstan, the number of people living with HIV (PLHIV) has increased steadily by 39% since 2010. Development of antiretroviral therapy (ART) resistance mutations (ARTRM) is a major hurdle in achieving effective treatment and prevention against HIV. Using HIV pol sequences from 602 PLHIV from Kazakhstan, we analyzed ARTRMs for their association with factors that may promote development of ARTRMs. 56% PLHIV were infected with HIV subtype A6 and 42% with CRF02_AG. The ARTRM Q174K was associated with increased viral load and decreased CD4+ cell count, while infection with CRF02_AG was associated with a lower likelihood of Q174K. Interestingly, CRF02_AG was positively associated with the ARTRM L10V that, in turn, was observed frequently with darunavir administration. Infection with CRF02_AG was positively associated with the ARTRM S162A that, in turn, was frequently observed with the administration of nevirapine, also associated with lower CD4 counts. Zidovudine or Nevirapine receipt was associated with the development of the ARTRM E138A, that, in turn, was associated with lower CD4 counts. Determination of a patient's HIV variant can help guide ART choice in Kazakhstan. For example, PLHIV infected with CRF02_AG will benefit less from darunavir and nevirapine, and emtricitabine should replace zidovudine.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".