How to Best Define Patients with Moderate Chronic Kidney Disease
Bibliographic record
Abstract
Background: The objective of this study was to identify which formula may best identify moderate chronic kidney disease (CKD) (glomerular filtration rate (GFR) cut-off of 60 ml/min/1.73 m2). Methods: We compared the performances of 14 serum creatinine (Scr) and 11 cystatin C (Cys C) estimated GFR equations using inulin clearance (Clin) as the reference test in a stable CKD population of 101 patients. Scatter, coefficient of variation, bias, precision, accuracy within 30% ranges from the reference method, agreements and receiving operating characteristics (ROC) of each test were compared. Results: ROC analysis identified Davis, Salzar, Virga and Cockcroft-Gault as the most sensitive (≥85%) and the isotope dilution mass spectrometry (IDMS), Edwards, MacIsaac as the most specific (95%) to define the GFR cut-off level of 60 ml/min/1.73 m2. Area under the ROC curve (AUC) was generally >0.8 (p ≤ 0.0001). 2 × 2 contingency tables to define CKD demonstrated sensitivity of 90% for Davis, while the IDMS was the most specific (95%). Among the Cys-C-based equations, Filler was the most sensitive (83%) and MacIsaac was the most specific (95%). Conclusion: The current equations lack consistent good performance to define CKD. The MDRD-IDMS equation missed 30% but demonstrated a high specificity to confirm those with moderate CKD. A combination of two equations, one sensitive and another specific, may be required for epidemiological studies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".