Correlation between Lymphocyte CD4 Count, Treatment Duration, Opportunistic Infection and Cognitive Function in Human Immunodeficiency Virus-Acquired Immunodeficiency Syndrome (HIV-AIDS) Patients
Bibliographic record
Abstract
BACKGROUND: Human immunodeficiency virus (HIV) infection is an epidemic worldwide, despite the marked benefits of antiretroviral therapy (ARV) in reducing severe HIV-associated dementia. A milder form of neurocognitive disorders are still prevalent and remain a challenge. AIM: This study aimed to determine the correlation between plasma cluster of differentiation 4 (CD4) lymphocyte, duration of ARV treatment, opportunistic infections, and cognitive function in HIV-AIDS patients. METHODS: A cross-sectional study involving 85 HIV-AIDS patients was conducted at Adam Malik General Hospital Medan, Indonesia. All subjects were subjected to physical, neurologic examination and Montreal Cognitive Assessment-Indonesian Version (MoCA-INA) to assess cognitive function and measurement of lymphocyte CD4 counts. RESULTS: Out of the 85 subjects evaluated, the proportion concerning sexes include 52 males (61.2 %) and 33 females (38.8%). The mean age was 38.53 ± 9.77 years old. There was a significant correlation between CD4 lymphocyte counts and MoCA-INA score (r = 0.271, p = 0.012), but there was no significant correlation between duration of ARV treatment and MoCA-INA score. There was also no difference in MoCA-INA score based on the presence of opportunistic infection. CONCLUSION: Lymphocyte CD4 count was independently correlated with cognitive function in HIV-AIDS patients.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".