How to Best Define Patients with Moderate Chronic Kidney Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 m(2)). METHODS: We compared the performances of 14 serum creatinine (S(cr)) and 11 cystatin C (Cys C) estimated GFR equations using inulin clearance (Cl(in)) 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 (>or=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 m(2). Area under the ROC curve (AUC) was generally >0.8 (p <or= 0.0001). 2 x 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it