Nonfinancial Factors Associated With Decreased Plasma Viral Load Testing in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To examine whether individual characteristics were associated with differential use of viral load testing when testing is available without charge to all HIV-positive patients with provincial health insurance. METHODS: Individuals enrolled in the HIV Ontario Observational Database with complete medication records and health insurance numbers for linkage were studied. Generalized estimating equation regression models were used to examine the relationship between time-varying covariates such as plasma viral load levels, CD4 counts, and antiretroviral regimen characteristics and the number of days between viral load tests and the occurrence of an interval of >or=6 or 9 months between tests. RESULTS: A total of 1032 individuals were included in the analysis with a median follow-up of 4.6 years and a median of 18 viral load tests. In multivariate analyses, clinically important gaps in viral load testing were more likely among injection drug users (odds ratio [OR]=1.86, P<0.0001), in more recent years (P<0.01) and for individuals not using antiretrovirals (OR=1.70, P<0.0001) and less likely among individuals using >4 antiretrovirals (OR=0.62, P<0.0001). Results were similar when the outcome was the number of days between tests. CONCLUSIONS: Injection drug users, younger individuals, and residents of Toronto used fewer viral load tests than other individuals, even when financial barriers to testing were removed.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".