Criteria for initiating highly active antiretroviral therapy and short‐term immune response among HIV‐1‐infected patients in Côte d'Ivoire
Bibliographic record
Abstract
OBJECTIVES: The aims of this study were to determine the predictors of CD4 count below 200 cells/microL and to propose an algorithm for antiretroviral therapy initiation; and to assess the determinants of immune response to highly active antiretroviral therapy (HAART) in Côte d'Ivoire. METHODS: A total of 615 consecutive patients attending an HIV/AIDS day hospital were enrolled in the study. We constructed a score system based on the results of a multivariate logistic regression analysis of the predictors of CD4 count <200 cells/microL with the intention of proposing an algorithm able to accurately designate patients eligible for HAART. We also identified factors associated with a short-term increase in CD4 count >50 cells/microL after HAART initiation. RESULTS: Total lymphocyte count <1200 cells/microL (P<0.0001), lower haemoglobin levels (P<0.0001), and Centers for Disease Control and Prevention (CDC) clinical stages C (P=0.005) and B (P=0.045), as compared with stage A, were associated with CD4 count <200 cells/microL. Nonetheless, no accurate algorithm for HAART initiation was found. Three hundred and three of the 615 patients were treated. Of these 303 patients, 79.5% showed an increase of >50 cells/microL in CD4 count 6 months after HAART initiation (median increase 128 cells/microL). Adherence >or=95% (P=0.022) and increase in absolute total lymphocyte count during follow-up (P<0.0001) were associated with a short-term positive immune response. CONCLUSIONS: Our results support the effectiveness of generic drug combinations in sub-Saharan Africa. In order to enhance the management of HIV disease in sub-Saharan Africa, efforts should target the development of low-cost CD4 cell count laboratory tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".