A Multi-State Model Examining Patterns of Transitioning Among States of Engagement in Care in HIV-Positive Individuals Initiating Combination Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: Common measures of engagement in care fail to acknowledge that infrequent follow-up may occur either intentionally among patients with sustained virologic suppression or unintentionally among patients with poor clinical outcomes. METHODS: Five states of HIV care were defined within the Canadian Observational Cohort Collaboration following combination antiretroviral therapy (cART) initiation: (1) guidelines HIV care [suppressed viral load (VL) and CD4 >200 cells per cubic millimeter, no gaps in cART >3 months, no gaps in CD4 or VL measurement >6 months], (2) successful care with decreased frequency of follow-up (as above except no gaps in CD4 or VL measurement >12 months), (3) suboptimal care (unsuppressed VL, CD4 <200 cells per cubic millimeter on 2 consecutive visits, ≥1 gap in cART >3 months, or ≥1 gap in CD4 or VL measurement >12 months), (4) loss to follow-up (no contact for 18 months), and (5) death. Multi-state models were used to determine factors associated with transitioning among states. RESULTS: In total, 7810 participants were included. Younger age, female gender, Indigenous ethnicity, and people who have injected drugs were associated with increased likelihoods of transitioning from guidelines to suboptimal care and decreased likelihoods of transitioning from suboptimal to guidelines care. One-fifth of individuals in successful, decreased follow-up after cART initiation (mean sojourn time 0.72 years) were in suboptimal care in subsequent years. CONCLUSIONS: Using routinely collected data, we have developed a flexible framework that characterizes patient transitions among states of HIV clinical care. We have demonstrated that multi-state models provide a useful approach to supplement "cascade of care" work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".