Using HIV Viral Load From Surveillance to Estimate the Timing of Antiretroviral Therapy Initiation
Bibliographic record
Abstract
INTRODUCTION: HIV surveillance programs do not typically collect comprehensive data on antiretroviral therapy (ART). We validated a population-based measure of ART initiation that uses HIV viral load (VL) results in the absence of data on ART. METHODS: We used CD4/VL data reported to NYC HIV Surveillance for persons aged ≥13 years and diagnosed with HIV from 2006 to 2012 to validate estimates of ART initiation date based on 3 ART initiation definitions: (1) ≥1-log decline in copies per milliliter between 2 VLs over 3 months; (2) ≥2-log decline in copies per milliliter between 2 VLs over 3 months; and (3) the earliest of either a ≥1-log decline in VL over 3 months, or a change from detectable VL to undetectable VL (<400 copies/mL) over any interval. We plotted median CD4 counts by quarter before and after ART initiation to compare estimated initiation date with nadir of the CD4 trajectory. RESULTS: A total of 24,348 persons were diagnosed with HIV in NYC from 2006 to 2012. In all, 12,123 persons had probable ART initiation based on ≥2-log decline, 12,719 based on ≥1-log decline, and 14,311 based on ≥1-log decline or detectable-undetectable change. Lowest median CD4 count occurred at the estimated ART initiation date for all 3 definitions. The definition based on a ≥1-log VL decline or a change from detectable to undetectable VL captured more ART initiations and identified earlier initiation dates. CONCLUSIONS: Serial VL measures are a valid source for estimating ART initiation. A definition that includes a ≥1-log VL decline or a change from detectable to undetectable VL performed best.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".