Adherence to Antiretroviral Therapy and Cd4 T-Cell Count Responses among HIV-Infected Injection Drug Users
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it