The impact of routine HIV drug resistance testing in Ontario: A controlled interrupted time series study
Bibliographic record
Abstract
BACKGROUND: Knowledge of HIV drug resistance informs the choice of regimens and ensures that the most efficacious options are selected. In January 2014, a policy change to routine resistance testing was implemented in Ontario, Canada. The objective of this study was to investigate the policy change impact of routine resistance testing in people with HIV in Ontario, Canada since January 2014. METHODS: We used data on people with HIV living in Ontario from administrative databases of the Institute for Clinical Evaluative Sciences (ICES) and Public Health Ontario (PHO), and ran ordinary least squares (OLS) models of interrupted time series to measure the levels and trends of 2-year mortality, 2-year hospitalizations and 2-year emergency department visits before (2005-2013) and after the policy change (2014-2017). Outcomes were collected in biannual periods, generating 18 periods before the intervention and 8 periods after. We included a control series of people who did not receive a resistance test within 3 months of HIV diagnosis. RESULTS: Data included 12,996 people with HIV, of which 8881 (68.3%) were diagnosed between 2005 and 2013, and 4115 (31.7%) were diagnosed between 2014 and 2017. Policy change to routine resistance testing within 3 months of HIV diagnosis led to a decreasing trend in 2-year mortality of 0.8% every six months compared to the control group. No significant differences in hospitalizations or emergency department visits were noted. INTERPRETATION: The policy of routine resistance testing within three months of diagnosis is beneficial at the population level.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".