COVID-19 outcomes in HIV patients: A review
Bibliographic record
Abstract
The effect of COVID-19 is enormous, and high-risk COVID-19 case arises when underlying infections like diabetes, chronic obstructive pulmonary disease, heart failure, coronary artery disease, or cardiomyopathy are present, and an immunocompromised state such as Human Immunodeficiency Virus (HIV). People living with HIV(PLHIV) may be exposed to severe COVID-19, mostly in areas with poor access to proper care and complex intervention for HIV infection. During the lockdown, those with medical appointments will not access health facilities, which may be detrimental to people living with HIV. Emerging evidence suggests COVID-19 pandemic fear may lead to adverse mental health outcomes and affect preventive behavior. In addition to the stigma and discrimination associated with HIV, COVID-19 is also causing concerns. People with HIV tend to have mental health issues such as depression, anxiety, and post-traumatic stress (PTSD), which can be both a cause and a harmful impact of HIV. Discussed in this research is the effect of the COVID-19 pandemic on HIV patients, their similarities, differences, and urgent attention from healthcare centers to take charge and respond to patients with HIV and other immunosuppressed conditions during the COVID-19 pandemic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".