Increase in transmitted HIV drug resistance among persons undergoing genotypic resistance testing in Ontario, Canada, 2002-09
Bibliographic record
Abstract
OBJECTIVES: To characterize persons undergoing HIV genotypic resistance testing (GRT) while treatment naive and to estimate the prevalence of transmitted HIV drug resistance (TDR) among HIV-positive outpatients in Ontario, Canada. METHODS: We analysed data from a multi-site cohort of persons receiving HIV care. Data were obtained from medical chart abstractions, interviews and record linkage with the Public Health Laboratories, Public Health Ontario. The analysis was restricted to 626 treatment-naive persons diagnosed in 2002-09. TDR mutations were identified using the calibrated population resistance tool. We used descriptive statistics and regression methods to characterize treatment-naive GRT test uptake and patterns of TDR. RESULTS: Overall, 53.2% (333/626) of participants had baseline GRT. The proportion increased with year of HIV diagnosis from 30.0% in 2002 to 82.6% in 2009 (P < 0.0001). Among those tested, 13.6% (CI 9.9-17.3%) had one or more drug resistance mutations, and 8.8% (CI 5.7-11.8%), 4.8% (CI 2.5-7.2%) and 2.7% (CI 1.0-4.5%) had mutations conferring resistance to nucleoside/tide reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs), respectively. TDR prevalence increased from 2002-07 to 2008-09 (adjusted OR 3.7, 95% CI 1.7-8.2), driven by a higher proportion with NRTI (18.2% versus 5.9%, P = 0.0009) and NNRTI mutations (11.7% versus 2.8%, P = 0.004) in the later time period. PI TDR remained unchanged. CONCLUSIONS: Baseline GRT increased dramatically since 2002, but remains below 100%. The prevalence of overall TDR tripled due to increases in NRTI and NNRTI mutations. These findings highlight the value of routine baseline GRT for TDR surveillance and patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".