Clinical and sociodemographic correlates of emergent or evolving HIV drug resistance in low viral load specimens in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND/METHODS: Treatment guidelines recommend genotypic HIV drug resistance testing (DRT) at virologic failure, typically for plasma viral loads (pVL) > 1000 HIV RNA c/mL. In some settings, DRT can be performed on low viral load (LVL) samples (pVL of 50-250 c/mL); however, such testing is resource-intensive and its clinical benefit is unclear. Therefore, we investigated the frequency and factors associated with emergent resistance in LVL samples using a comprehensive, provincial database of HIV Protease-Reverse Transcriptase and Integrase sequences. RESULTS: A total of 43,979 Protease-RT DRTs were performed in British Columbia between 1999 and 2022, of which 2970 (6.8 %) were on LVL samples. Testing was successful for 1575 (53.0 %) LVL samples compared to 81.4 % and 94.4 % of samples with pVL 250-999 and pVL ≥ 1000 c/mL, respectively (p < 0.001). Compared to prior genotypes collected from samples with pVL > 250 c/mL, a total of 104 (7.3 %) cases of new or evolving drug resistance were identified from 1423 LVL DRTs. Of these, 49.5 %, 42.9 % and 22.9 % exhibited new Nucleoside Reverse Transcriptase, Non-Nucleoside Reverse Transcriptase and Protease resistance, respectively. Of 9309 Integrase DRTs performed between 2008 and 2022, only 4 (1.2 %) cases of new or evolving integrase resistance were observed. Multivariable analyses identified clinical/sociodemographic factors significantly associated with emergent resistance, including time elapsed between DRTs, historic cumulative resistance and NNRTI-based antiretroviral therapy. CONCLUSIONS: Emergent or evolving resistance is identified infrequently in low viral load specimens. Given its resource-intensive nature, resistance testing of low viral load specimens may not be generally warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".