HIV viral load trajectories of women living with HIV in Metro Vancouver, Canada
Bibliographic record
Abstract
This study describes long-term viral load (VL) trajectories and their predictors among women living with HIV (WLWH), using data from Sexual Health and HIV/AIDS: Women’s Longitudinal Needs Assessment (SHAWNA), an open prospective cohort study with linkages to the HIV/AIDS Drug Treatment Program. Using Latent Class Growth Analysis (LCGA) on a sample of 153 WLWH (1088 observations), three distinct trajectories of detectable VL (≥50 copies/ml) were identified: ‘sustained low probability of detectable VL’, characterized by high probability of long-term VL undetectability (51% of participants); ‘ high probability of delayed viral undetectability’, characterized by a high probability VL detectability at baseline that decreases over time (43% of participants); and ‘ high probability of detectable VL’, characterized by a high probability of long-term VL detectability (7% of participants). In multivariable analysis, incarceration (adjusted odds ratio (AOR) = 3.24; 95%CI:1.34–7.82), younger age (AOR = 0.96; 95%CI:0.92–1.00), and lower CD4 count (AOR = 0.82; 95%CI:0.72–0.93) were associated with ‘ high probability of delayed viral undetectability’ compared to ‘sustained low probability of detectable VL.’ This study reveals the dynamic and heterogeneous nature of WLWH’s long-term VL patterns, and highlights the need for early engagement in HIV care among young WLWH and programs to mitigate the destabilizing impact of incarceration on WLWH’s HIV treatment outcomes.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".