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: ; 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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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".