Effects of CD4 Cell Counts and Viral Load Testing on Mortality Rates in Patients With HIV Infection Receiving Antiretroviral Treatment: An Observational Cohort Study in Rural Southwest China
Bibliographic record
Abstract
BACKGROUND: Recent studies have suggested that CD4 cell count monitoring has little added value in patients who are virologically suppressed and immunologically stable if viral load (VL) testing is routinely available. These conclusions have not been directly assessed using mortality rate as a study end point in a real-world setting. METHODS: This human immunodeficiency virus (HIV) treatment cohort study from 2008 to 2014 was conducted in Guangxi, China. We used a Cox regression model to analyze associations between the frequency of CD4 cell counts and VL testing and death. RESULTS: Compared with monitoring CD4 cell counts ≥3 times during the first year of antiretroviral therapy (ART) initiation, as currently suggested by the Chinese National Free Antiretroviral Treatment Program, monitoring them less than twice during the first year of ART was significantly associated with death; however, monitoring them twice in that year did not significantly increase mortality rates. Compared with testing VL at least once during the first year of ART, as currently suggested by the National Free Antiretroviral Treatment Program, performing no VL tests in the first year after ART initiation was significantly associated with higher mortality rates. Routine CD4 cell count monitoring did not have an impact on mortality rates among HIV-infected patients with VLs <1000 copies/mL or CD4 cell counts ≥350/μL beyond 12 months after ART initiation. CONCLUSIONS: Our study suggests that CD4 cell counts can be reduced to twice during the first year of ART and be reduced or stopped for patients who have achieved virologic suppression or immunologic stability after 12 months of treatment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".