A Description of the Clinical and Socio-demographic Factors, Specifically Medication Coverage, Associated with Virologic Suppression of Those Living with HIV in Manitoba
Bibliographic record
Abstract
With antiretroviral therapy (ART), those living with Human Immunodeficiency Virus (HIV) have a life-expectancy comparable to the general population. Virologic suppression, a treatment goal defined as an HIV-1 RNA viral load of less than 200 copies/mL, is associated with better health outcomes and often a negligible risk of viral transmission. Virologic suppression hinges on accessible ART, optimal adherence, and long-term engagement in care. Manitobans can access ART through any of the following drug coverage programs, each with their own set of eligibility criteria: Canadian Forces Health Services (CFHS), Interim Federal Health Program (IFHP), Non-Insured Health Benefits (NIHB), Manitoba Pharmacare, Employment and Income Assistance (EIA), private insurance, programs from other provinces, through clinical trials, compassionate supply via pharmaceutical companies, or no coverage at all. In this project we used participant socio-demographic data with clinical data (viral load, CD4 counts, and drug coverage program) collected at distinct points in 2016 and 2017. In 2016, people who inject drugs (PWID) and those with coverage via NIHB or EIA had lower rates of viral suppression than others without those characteristics. That association was no longer seen for PWID and EIA coverage in 2017, with NIHB coverage being the only significant predictor for an unsuppressed viral load. Although many Manitobans can access their ART at little or no out of pocket cost, this is insufficient without other interventions that address systemic issues which have caused the social inequities which may lead to sub-optimal adherence and decreased engagement with care within populations associated with virologic failure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".