A comparison of measured and estimated glomerular filtration rate in successfully treated HIV-patients with preserved renal function
Bibliographic record
Abstract
BACKGROUND: Monitoring of renal function becomes increasingly important in the aging population of HIV-1 infected patients. We compared Cockroft & Gault (C&G), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), Modification of Diet in Renal Disease (MDRD), Cystatin C- and 24 h urine-based estimated GFR (eGFR) with the gold standard, measured GFR (mGFR) using [125I]-iothalamate. METHODS: Substudy within a randomized, multinational trial comparing continuing zidovudine/ lamivudine with switching to tenofovir/ emtricitabine in patients with suppressed HIV-1 infection. Accuracy (defined as the mean difference between eGFR and mGFR) and precision (defined as standard deviation (SD) of the mean difference between eGFR and mGFR) of the eGFRs were calculated using linear regression and Bland & Altman analysis. RESULTS: We included 19 patients, 18 men, 15 Caucasian, mean (SD) age 46.0 y (± 8.9) and BMI 23.9 kg/m2 (± 3.0). Mean (SD) mGFR was 102 ml/min/1.73 m2 (± 19), 4 patients had mild renal dysfunction. All eGFRs tended to underestimate true GFR, with best accuracy for C&G (-1 ml/min/1.73 m2), CKD-EPI (-1 ml/min/1.73 m2), 24 hcreatinine clearance (-2 ml/min/1.73 m2) and MDRD-6 (0 ml/min/1.73 m2), and worst for cystatin C-based (-9 ml/min/1.73 m2) and MDRD-4 estimations (-10 ml/min/1.73 m2). Accuracy worsened at higher mGFR, but was not significantly influenced by age. C&G tended to overestimate at higher BMI. Precision was comparable for all GFR estimations. CONCLUSIONS: In this limited number of patients with preserved renal function and suppressed HIV-infection C&G and CKD-EPI appeared to be the best reflection of real GFR and most practical tool for monitoring GFR.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".