A Randomized Study of Serial Telephone Call Support to Increase Adherence and Thereby Improve Virologic Outcome in Persons Initiating Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: Adherence to antiretroviral therapy is difficult, and methods to increase it are needed. METHODS: We tested the impact of supportive telephone calls in an adherence substudy of a treatment trial. Subjects initiating antiretroviral therapy received either each site's usual adherence support measures or usual support measures and scripted serial telephone calls (16 calls during 96 weeks). RESULTS: A total of 282 subjects enrolled: 140 in the usual support measures group and 142 in the calls group. A total of 75% of expected calls were completed. Virologic failure occurred in 97 (34%) subjects: 52 (37%) of those in the usual support measures group and 45 (32%) of those in the calls group; time to virologic failure was not different (P=.32). In each group, >72% of subjects reported > or =95% adherence, with no difference between groups. Independent predictors of higher rates of virologic failure were <95% adherence, receiving the 4-drug regimen with nelfinavir, and female sex; older age was associated with decreased likelihood of virologic failure. Receiving the 4-drug regimen with nelfinavir, higher stress scores, older age, and higher call completion rates were independently associated with higher adherence. CONCLUSIONS: Serial telephone calls did not improve virologic outcome but had an impact on self-reported adherence.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".