Bibliographic record
Abstract
PURPOSE OF REVIEW: Chronic kidney disease (CKD) is common in people living with HIV (PLWH) and is related to a multitude of factors. The aim of this review is to provide an overview of the most recent evidence of renal adverse effects of antiretroviral drugs, predictors of CKD risk and areas for future research. RECENT FINDINGS: Advancing age, cardiometabolic risk factors and adverse effects of antiretroviral drugs contribute to the higher prevalence of CKD in PLWH. Genetic factors and baseline clinical CKD risk are strongly correlated to risk of incident CKD, although it is unclear to what extent gene polymorphisms explain renal adverse effects related to tenofovir disoproxil fumarate (TDF). Switching from TDF to tenofovir alafenamide (TAF) in people with baseline renal dysfunction improves renal parameters; however, the long-term safety and benefit of TAF in individuals at low risk of CKD is an area of ongoing research. SUMMARY: Several factors contribute to estimated glomerular function decline and CKD in PLWH. Clinical risk scores for CKD may be useful to inform selection of ART in an ageing population. In people with baseline renal dysfunction, potentially nephrotoxic antiretroviral drugs should be avoided.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.016 | 0.004 |
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".