Adverse Event Risk Assessment on Patients Receiving Combination Antiretroviral Therapy in South Africa
Bibliographic record
Abstract
Purpose: To determine the risk factors for the development of serious adverse events (AEs) in black adult patients on combination antiretroviral therapy (cART). Methods: This prospective cohort study consisted of 368 adult black HIV positive patients receiving cART at the Grey’s Hospital, KwaZulu-Natal, South Africa. Patients were intensively monitored for incidence of adverse events and the factors associated with their development, under the Antiretroviral Cohort Adverse Event Monitoring in KwaZulu-Natal (ACADEMIK). Multiple logistic regression models were used to identify the risk factors for AEs. Results: A total of 406 AEs were reported across the 13 patient hospital visits in the study. Peripheral neuropathy was the most prevalent adverse event (16%), followed by hypercholesterolaemia (14%), lipoatrophy/lipodystrophy (13%) and skin reaction (11%). Cluster differentiation (CD4) counts (p = 0.0280), age (p = 0.0227) and weight (p = 0.0017) were identified as the significant predictors for hypercholesterolaemia, while sex (p = 0.0309) was significant with respect to skin reaction. CD4 counts (p=0.0200) was also significant for lipoatrophy/lipodystrophy. Skin reaction (23%), diarrhea (18%), hypercholesterolaemia (15%), thrombocytopenia (15%) and peripheral neuropathy (13%) were the top five most incident AEs. Overall, about 46% of the regimens administered were tenofovir-based and 31% zidovudine-based. Conclusions: To enhance the prevention of hypercholesterolaemia, lipoatrophy/lipodystrophy and skin reaction among black adult HIV positive patients on cART, we recommend that CD4 counts and weight be closely monitored and documented during clinic visits.
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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 | 0.003 |
| 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) | 0.001 | 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".