Notable transmitted HIV drug resistance among people who inject drugs in Pakistan
Bibliographic record
Abstract
Abstract Transmission of drug-resistant HIV strains to treatment-naïve patients can compromise antiretroviral therapy (ART) effectiveness and lead to treatment failure. In Pakistan, transmitted HIV drug resistance among people who inject drugs (PWID) is fuelled by a lack of ART, poor drug adherence, and unsafe injection practices, resulting in efficient transmission in large injecting networks. A cross-sectional study was conducted among PWID recruited in the Pakistani cities of Karachi, Larkana, Peshawar, Quetta and Hyderabad (August 2014 to January 2015). A portion of the HIV pol gene was amplified from HIV-reactive dried blood spot specimens ( n =282/367) and sequenced using an in-house Sanger sequencing assay for HIV drug resistance mutation genotyping. Drug resistance mutations (DRMs) were identified using the Stanford University HIV Drug Resistance Database HIVdb algorithm ( https://hivdb.stanford.edu/hivdb ). Overall, HIV subtype A1 was dominant (78.0%; n =220), followed by CRF02_AG (15.6%; n =44), CRF35_AD (2.5% n =7), recombinants (3.5%; n =10), and subtype C (0.4% n =1). DRM analysis identified over half (63.8%) of participants harbored at least one DRM, of which 28.9% reported using help from a professional injector. Nearly all (99.4%) participants were not actively receiving ART because most (88.7%) had never undergone HIV testing and were unaware of their status. Findings suggest significant transmitted HIV drug resistance present among PWID, exacerbated by unsafe injection practices, particularly professional injection. Low testing rates signal a need for more comprehensive testing programs to improve HIV status awareness and ART coverage in Pakistan. Given most treatment-naïve participants had evidence of drug resistance, drug resistance genotyping prior to ART initiation might aid in ensuring effective treatment to prevent transmission of resistant HIV strains.
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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 | 0.001 | 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) | 0.003 | 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".