Antiretroviral treatment toxicity is the next challenge in HIV/AIDS management: institutional-based cross-sectional study
Bibliographic record
Abstract
Introduction: Even though the contribution of antiretroviral drugs is undeniable in the treatment of HIV/AIDS, they can cause mild to serious adverse effects.These drug-related side effects are considered reasons for change of treatment regimen, discontinuation, and poor adherence.The objective of this study was to assess the magnitude of associated factors of antiretroviral treatment (ART) toxicity in adult HIV-positive patients on ART. Material and methods:A cross-sectional study was conducted among a total of 404 study participants.Both primary and secondary data were utilized.Primary data was collected by using questionnaires and physical examination.Secondary data were extracted from patients' charts by using a checklist.Binary logistic regression was applied to determine the associated with ART toxicity factors.Factors with a p-value of < 0.05 were recognized as statistically significant.Results: Of 404 study participants, 68 (16.8%) experienced ART toxicity.Patients with opportunistic infections (p < 0.001) and those taking cotrimoxazole preventive therapy (CPT) (p = 0.045) were at higher risk of developing ART toxicity.Viral load (p = 0.02), WHO staging (p < 0.05), and media unavailability (p = 0.045) were also significantly associated factors.Conclusions: Antiretroviral toxicity was higher in patients with an opportunistic infection, advanced WHO stage, increased viral load, and on CPT.Media availability was also an important factor.Therefore, healthcare providers should closely follow HIV/AIDS patients on CPT, advanced WHO stage, and those with increased viral load.HIV/AIDS patients with opportunistic infections need to be monitored carefully.Alternative information channels, which can be easily accessible, need to be considered by all stakeholders.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".