Temporal trends of transmitted HIV drug resistance in a multinational seroconversion cohort
Bibliographic record
Abstract
BACKGROUND: The rate of transmitted drug resistance (TDR) may increase with wider use of antiretroviral therapy and can contribute to therapeutic failure. We analysed time trends in TDR among HIV seroconverters. METHODS: Using CASCADE data of individuals with well estimated dates of HIV seroconversion, we examined HIV nucleotide sequences collected prior to antiretroviral therapy use from 1996-2012. All samples were taken within 12 months of testing HIV positive. Using logistic regression, we examined the association between TDR and year of seroconversion, adjusting for confounders. RESULTS: Of 4717 individuals seroconverting between 1996 and 2012, median (IQR) age at seroconversion was 33 (27, 39) years. The majority (3839; 92%) were male, mainly exposed through MSM (3767; 80%), and infected with subtype B (3464; 73%). Overall, 515 (11%) individuals had at least one drug resistance-related mutation; 280 individuals with nucleoside reverse transcriptase, 185 with nonnucleoside reverse transcriptase, and 144 with protease inhibitor mutations. Estimated TDR prevalence was 19.4% (8.2, 36.0) in 1996, significantly decreasing to 8.5% (5.9, 11.9) in 2012 [odds ratio (OR; 95% confidence interval (CI)) = 0.92 (0.90, 0.95) per year increase]. Individuals exposed through sex between men and women were significantly less likely to have been infected with a drug-resistant strain [OR (95% CI) = 0.59 (0.41, 0.87) compared with MSM], and there was marginal evidence that sampling during acute infection was associated with higher odds of resistance [OR (95% CI) = 1.20 (0.97, 1.7), P = 0.093] compared with later sampling. CONCLUSION: TDR has decreased over calendar time although a significant proportion of new infections still carry resistance-related mutations.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".