Adherence to Antiretroviral Therapy and Cd4 T-Cell Count Responses among HIV-Infected Injection Drug Users
Bibliographic record
Abstract
OBJECTIVE: To evaluate the time to CD4 cell count response (> or = 50 cells/mm3) among patients initiating highly active antiretroviral therapy (HAART) with and without a history of injection drug use, and to examine the potential role of non-adherence to HAART on differential CD4 responses. METHODS: Population-based analysis of treatment-naive patients initiating HAART during the period 1 August 1996 to 31 July 2000 and who were followed until 31 March 2002. Patients were stratified based on 95% adherence and history of injection drug use, and Kaplan-Meier methods and Cox regression were used to evaluate CD4 response rates and factors associated with CD4 responses. RESULTS: Overall, the CD4 cell count response rate was slower among injection drug users in Kaplan-Meier analyses (log-rank: P<0.05). However, no differences existed when the analyses were restricted to adherent patients (log-rank: P=0.349). Similarly, the differences in the time to CD4 cell count response observed in univariate Cox regression analyses for patients with a history of injection drug use [relative hazard: 0.85 (95% CI: 0.75-0.97)] diminished after adjustment for adherence [adjusted relative hazard: 1.02 (95% CI: 0.89-1.16)]. CONCLUSION: These data demonstrate the importance of adherence on CD4 cell count responses and highlight the need for interventions to improve antiretroviral adherence among injection drug user.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".