Predictive Factors of Detectable Viral Load in HIV-Infected Patients
Bibliographic record
Abstract
Despite availability of effective antiretroviral therapy (ART), many HIV patients still have a detectable viral load (VL). Predictive factors of detectable VL are not well documented. This study was done at two large multidisciplinary HIV outpatient clinics at the Centre hospitalier de l'Université de Montréal (CHUM) and the McGill University Health Centre (MUHC). This is a retrospective case-control study of patients treated between 2016 and 2018. Cases had a VL ≥50 copies/mL in 2018. Controls had an undetectable VL from 2016 to 2018. Matching was based on gender and year of HIV diagnosis. Primary objective was to identify predictive factors of detectable VL. Secondary objectives included to identify predictive factors of virologic failure, low persistent viremia, and viral blip. A forward stepwise model selection by the Akaike Information Criterion of the conditional logistic regression was used to identify predictive factors. Two hundred cases were identified and matched with 200 controls. The cohort was mostly male (68.0%) with a median age of 54 years (21-83 years). Among cases, viral blip was the most common type of detectable VL (43.0%). The strong predictive factors for a detectable VL were adherence to ART and seeking health care services. Asylum seekers were less at risk of detectable VL. Adherence to ART was the only strong predictive factor for virologic failure. Three main predictive factors of detectable VL were identified in two ambulatory clinic hospitals in Montreal. Ascertaining these factors will allow for identification of patients more at risk of detectable VL.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".