Serious Non-AIDS events: Immunopathogenesis and interventional strategies
Bibliographic record
Abstract
Despite the major advances in the management of HIV infection, HIV-infected patients still have greater morbidity and mortality than the general population. Serious non-AIDS events (SNAEs), including non-AIDS malignancies, cardiovascular events, renal and hepatic disease, bone disorders and neurocognitive impairment, have become the major causes of morbidity and mortality in the antiretroviral therapy (ART) era. SNAEs occur at the rate of 1 to 2 per 100 person-years of follow-up. The pathogenesis of SNAEs is multifactorial and includes the direct effect of HIV and associated immunodeficiency, underlying co-infections and co-morbidities, immune activation with associated inflammation and coagulopathy as well as ART toxicities. A number of novel strategies such as ART intensification, treatment of co-infection, the use of anti-inflammatory drugs and agents that reduce microbial translocation are currently being examined for their potential effects in reducing immune activation and SNAEs. However, currently, initiation of ART before advanced immunodeficiency, smoking cessation, optimisation of cardiovascular risk factors and treatment of HCV infection are most strongly linked with reduced risk of SNAEs or mortality. Clinicians should therefore focus their attention on addressing these issues prior to the availability of further data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.001 |
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".