Drug resistance mutations after the first 12 months on antiretroviral therapy and determinants of virological failure in Rwanda
Bibliographic record
Abstract
OBJECTIVE: To evaluate HIV drug resistance (HIVDR) and determinants of virological failure in a large cohort of patients receiving first-line tenofovir-based antiretroviral therapy (ART) regimens. METHODS: A nationwide retrospective cohort from 42 health facilities was assessed for virological failure and development of HIVDR mutations. Data were collected at ART initiation and at 12 months of ART on patients with available HIV-1 viral load (VL) and ART adherence measurements. HIV resistance genotyping was performed on patients with VL ≥1000 copies/ml. Multiple logistic regression was used to determine factors associated with treatment failure. RESULTS: Of 828 patients, 66% were women, and the median age was 37 years. Of the 597 patients from whom blood samples were collected, 86.9% were virologically suppressed, while 11.9% were not. Virological failure was strongly associated with age <25 years (adjusted odds ratio [aOR]: 6.4; 95% confidence interval [CI]: 3.2-12.9), low adherence (aOR: 2.87; 95% CI: 1.5-5.0) and baseline CD4 counts <200 cells/μl (aOR 3.4; 95% CI: 1.9-6.2). Overall, 9.1% of all patients on ART had drug resistance mutations after 1 year of ART; 27% of the patients who failed treatment had no evidence of HIVDR mutations. HIVDR mutations were not observed in patients on the recommended second-line ART regimen in Rwanda. CONCLUSIONS: The last step of the UNAIDS 90-90-90 target appears within grasp, with some viral failures still due to non-adherence. Nonetheless, youth and late initiators are at higher risk of virological failure. Youth-focused programmes could help prevent further drug HIVDR development.
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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 | 0.001 | 0.002 |
| 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".