Cystatin C-Based Renal Function Changes After Antiretroviral Initiation: A Substudy of a Randomized Trial
Bibliographic record
Abstract
BACKGROUND: The effects of antiretrovirals on cystatin C-based renal function estimates are unknown. METHODS: We analyzed changes in renal function using creatinine and cystatin C-based estimating equations in 269 patients in A5224s, a substudy of study A5202, in which treatment-naive patients were randomized to abacavir/lamivudine or tenofovir/emtricitabine with open-label atazanavir/ritonavir or efavirenz. RESULTS: Changes in renal function significantly improved (or declined less) with abacavir/lamivudine treatment compared with tenofovir/emtricitabine using the Cockcroft-Gault formula (P = .016) and 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI; P = .030) and 2012 CKD-EPI cystatin C-creatinine (P = .025). Renal function changes significantly improved (or declined less) with efavirenz compared with atazanavir/ritonavir (P < .001 for all equations). Mean (95% confidence interval) renal function changes specifically for tenofovir/emtricitabine combined with atazanavir/ritonavir were -8.3 (-14.0, -2.6) mL/min with Cockcroft-Gault; -14.9 (-19.7, -10.1) mL/min per 1.73(2) with Modification of Diet in Renal Disease; -12.8 (-16.5, -9.0) mL/min per 1.73(2) with 2009 CKD-EPI; +8.9 (4.2, 13.7) mL/min per 1.73(2) with 2012 CKD-EPI cystatin C; and -1.2 (-5.1, 2.6) mL/min per 1.73(2) with 2012 CKD-EPI cystatin C-creatinine. Renal function changes for the other treatment arms were more favorable but similarly varied by estimating equation. CONCLUSIONS: Antiretroviral-associated changes in renal function vary in magnitude and direction based on the estimating equation used.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".