Differential impact of adherence on long-term treatment response among naive HIV-infected individuals
Bibliographic record
Abstract
OBJECTIVES: To examine the long-term impact of adherence on virologic, immunologic, and dual response stratified by type of HAART regimen in treatment-naive patients starting HAART in British Columbia, Canada; and to assess the degree of virologic and immunologic response associated with emergence of drug resistance, progression to AIDS, and mortality. METHODS: Eligible participants initiated HAART between 1 January 2000 and 30 November 2004, were followed until 30 November 2005, and had at least 2 years of follow-up. Virologic and immunologic responses were dichotomized at their median values. Virologic response was defined as at least 65% of follow-up time with plasma viral load (pVL) of less than 50 copies/ml. Immunologic response was defined as a CD4 cell count increase of at least 145 cells/microl. Adherence measures were based on prescription refill compliance. Proportional odds models and logistic regression were used to address our objectives. RESULTS: The distribution of patient responses was 394 (44.9%) for CD4+/pVL+ (best), 350 (39.9%) for CD4-/pVL+ or CD4+/pVL- (incomplete), and 134 (15.3%) for CD4-/pVL- (worst). We found a positive correlation between adherence and virologic and immunologic responses (P < 0.01). Having worst compared with best response (reference group) was associated with higher odds of mortality (odds ratio: 6.09; 95% confidence interval: 2.57-14.42) and emergence of drug resistance (odds ratio: 10.56; 95% confidence interval: 5.93-18.81) even after adjusting for adherence and HAART regimen. CONCLUSION: Patients not attaining the best virologic and immunologic responses are at a high risk for emergence of drug resistance and mortality, and these responses are highly dependent on the adherence level and initial HAART regimen. Patients on protease inhibitor-single did worse no matter the adherence level.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".