Quality of initial <scp>HIV</scp> care in Canada: extension of a composite programmatic assessment tool for <scp>HIV</scp> therapy
Bibliographic record
Abstract
Objectives To document the quality of initial HIV care in Canada using the Programmatic Compliance Score ( PCS ), to explore the association of the PCS with mortality, and to identify factors associated with higher quality of care. Methods We analysed data from the Canadian Observational Cohort Collaboration ( CANOC ), a multisite Canadian cohort of HIV ‐positive adults initiating combination antiretroviral therapy ( ART ) from 2000 to 2011. PCS indicators of noncompliance with HIV treatment guidelines include: fewer than three CD 4 count tests in the first year of ART ; fewer than three viral load tests in the first year of ART ; no drug resistance testing before initiation; baseline CD 4 count < 200 cells/mm 3 ; starting a nonrecommended ART regimen; and not achieving viral suppression within 6 months of initiation. Indicators are summed for a score from 0 to 6; higher scores indicate poorer care. Cox regression was used to assess the association between PCS and mortality and ordinal logistic regression was used to explore factors associated with higher quality of care. Results Of the 7460 participants (18% female), the median score was 1.0 (Q1−Q3 1.0−2.0); 21% scored 0 and 8% scored ≥ 4. In multivariable analysis, compared with a score of 0, poorer PCS was associated with mortality for scores > 1 [score = 2: adjusted hazard ratio ( AHR ) 1.64; 95% confidence interval ( CI ) 1.13–2.36; score = 3: AHR 2.02; 95% CI 1.38–2.97; score ≥ 4: AHR 2.14; 95% CI 1.43–3.21], after adjustments for age, sex, province, ART start year, hepatitis C virus ( HCV ) coinfection, and baseline viral load. Women, individuals with HCV coinfection, younger people, and individuals starting ART earlier (2000−2003) had poorer scores. Conclusions Our findings further validate the PCS as a predictor of all‐cause mortality. Disparities identified suggest that further efforts are needed to ensure that care is equitably accessible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".