Validating a self-report measure of HIV viral suppression: an analysis of linked questionnaire and clinical data from the Canadian HIV Women’s Sexual and Reproductive Health Cohort Study
Bibliographic record
Abstract
BACKGROUND: We assessed the validity of a self-report measure of undetectable viral load (VL) among women with HIV in British Columbia (BC), Canada. Questionnaire data from the Canadian HIV Women's Sexual and Reproductive Health Cohort Study was linked with population-based clinical data from the BC Centre for Excellence in HIV/AIDS. Self-reported undetectable VL was assessed by the question: "What was your most recent VL, undetectable (i.e. <50 copies/mL) or detectable (i.e. ≥50 copies/mL)?" Laboratory measurements of VL <50 copies/mL (closest to/before study visit) were the criterion for validity analyses. We measured positive and negative predictive values (PPV, NPV) and likelihood ratios (LR+, LR-). RESULTS: Of 356 participants, 99% were linked to clinical data. Those unlinked (n = 1), missing self-report VL (n = 18), or missing self-report and laboratory VL (n = 1) were excluded. Among the remaining 336: median age was 44 (IQR 37-51); 96% identified as cis-gender; 84% identified as heterosexual; and 45% identified as Indigenous, 40% White, 8% African, Caribbean, or Black, and 8% other/multiple ethnicities. Overall, 85% self-reported having an undetectable VL while 82% had clinical data indicating viral suppression. The PPV was 93.7 (95% CI 90.2-96.2) indicating that 94% of women who self-reported being undetectable truly were. The NPV was 80.4 (95% CI 66.9-90.2). LR+ was 3.2 (2.1-4.6) and LR- was 0.05 (0.03-0.10). CONCLUSIONS: Our self-report measure assessing undetectable VL strongly predicted true viral suppression among Canadian women with HIV. This measure can be used in research settings without laboratory data in regions with high rates of VL testing and suppression.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".