Characterizing retention in HAART as a recurrent event process
Bibliographic record
Abstract
OBJECTIVE: The benefits of HAART rely on continuous lifelong treatment retention. We used linked population-level health administrative data to characterize durations of HAART retention and nonretention. DESIGN: This is a retrospective cohort study. METHODS: We considered individuals initiating HAART in British Columbia (1996-2012). An HAART episode was considered discontinued if individuals had a gap of at least 30 days between days in which medication was prescribed. We considered durations of HAART retention and nonretention separately, and used Cox proportional hazards frailty models to identify demographic and treatment-related factors associated with durations of HAART retention and nonretention. RESULTS: Six thousand one hundred fifty-two individuals were included in the analysis; 81.2% were male, 40.6% were people who inject drugs, and 42.8% initiated treatment with CD4 cell count less than 200 cells/μl. Overall, 29% were continuously retained on HAART through the end of follow-up. HAART episodes were a median 6.8 months (25th, 75th percentile: 2.3, 19.5), whereas off-HAART episodes lasted a median 1.9 months (1.2, 4.5). In Cox proportional hazards frailty models, durations of HAART retention improved over time. Successive treatment episodes tended to decrease in duration among those with multiple attempts, whereas off-HAART episodes remained relatively stable. Younger age, earlier stages of disease progression, and injection drug use were all associated with shorter durations of HAART retention and longer off-HAART durations. CONCLUSION: Metrics to monitor HAART retention, dropout, and reentry should be prioritized for HIV surveillance. Clinical strategies and public health policies are urgently needed to improve HAART retention, particularly among those at earlier stages of disease progression, the young, and people who inject drugs.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".