Antiretroviral-Treated HIV-Infected Women Have Similar Long-Term Kidney Function Trajectories as HIV-Uninfected Women
Bibliographic record
Abstract
Natural history studies suggest increased risk for kidney function decline with HIV infection, but few studies have made comparisons with HIV-uninfected women. We examined whether HIV infection treated with highly active antiretroviral therapy (HAART) remains associated with faster kidney function decline in the Women's Interagency HIV Study. HIV-infected women initiating HAART with (n=105) or without (n=373) tenofovir (TDF) were matched to HIV-uninfected women on calendar and length of follow-up, age, systolic blood pressure, hepatitis C antibody serostatus, and diabetes history. Linear mixed models were used to evaluate differences in annual estimated glomerular filtration rate (eGFR). Person-visits were 4,741 and 11,512 for the TDF-treated and non-TDF-treated analyses, respectively. Mean baseline eGFRs were higher among women initiated on TDF-containing HAART and lower among those on TDF-sparing HAART compared to their respective HIV-uninfected matches (p<0.05 for both). HIV-infected women had annual rates of eGFR changes similar to HIV-uninfected matches (p-interaction >0.05 for both). Adjusting for baseline eGFR, mean eGFRs at 1 and 3 years of follow-up among women initiated on TDF-containing HAART were lower than their uninfected matches (-4.98 and -4.26 ml/min/1.73 m(2), respectively; p<0.05 for both). Mean eGFR of women initiated on TDF-sparing HAART was lower versus uninfected matches at 5 years (-2.19 ml/min/1.73 m(2), p=0.03). HAART-treated HIV-infected women had lower mean eGFRs at follow-up but experienced rates of annual eGFR decline similar to HIV-uninfected women. Tenofovir use in HIV-infected women with normal kidney function did not accelerate long-term kidney function decline relative to HIV-uninfected women.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".