Implementation and Operational Research
Bibliographic record
Abstract
BACKGROUND: Ensuring that people living with HIV are accessing and staying in care is vital to achieving optimal health outcomes including antiretroviral therapy (ART) success. We sought to characterize engagement in HIV care among participants of a large clinical cohort in Ontario, Canada, from 2001 to 2011. METHODS: The Ontario HIV Treatment Network Cohort Study (OCS) is a multisite HIV clinical cohort, which conducts record linkage with the provincial public health laboratory for viral load tests. We estimated the annual proportion meeting criteria for being in care (≥1 viral load per year), in continuous care (≥2 viral load per year ≥90 days apart), on ART, and with suppressed viral load <200 copies per milliliter. Ratios of proportions according to socio-demographic and clinical characteristics were examined using multivariable generalized estimating equations with a log-link. RESULTS: A total of 5380 participants were followed over 44,680 person-years. From 2001 to 2011, we observed high and constant proportions of patients in HIV care (86.3%-88.8%) and in continuous care (76.4%-79.5%). There were statistically significant rises over time in the proportions on ART and with suppressed viral load; by 2011, a majority of patients were on ART (77.3%) and had viral suppression (76.2%). There was minimal variation in HIV engagement indicators by socio-demographic and HIV risk characteristics. CONCLUSIONS: In a setting with universal health care, we observed high proportions of HIV care engagement over time and an increased proportion of patients attaining successful virologic suppression, likely due to improvements in ART regimens and changing guidelines.
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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 | 0.042 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".