Comparison of Two Creatinine Based Equations for Routine Estimation of GFR in a Speciality Clinic for Diabetes.
Bibliographic record
Abstract
OBJECTIVE: To compare the bias, absolute bias, precision and accuracies between the equations, viz., CKD-EPI (Scr), CKD-EPI (Scys) and MDRD in Indian patients with type 2 diabetes. METHODS: 198 patients who underwent 24 h urinary collection for assessing kidney function between November 2014-January 2015 were included. Cohen's κ coefficient, Bland-Altman plot were calculated between estimated kidney function equations, and bias, precision, accuracies was calculated between the formulae. RESULTS: The mean eGFR based on MDRD, CKD-EPI (Scr) and CKD-EPI (Scys) equations were 64.5±21.9, 70.2±25.1 and 74.7±31.0 ml/min/ 1.73m2 respectively. The overall mean absolute bias was smallest for MDRD vs CKD EPI (Scr). The precision was also least for MDRD vs CKD EPI (Scr) indicating that the agreement between these equations is consistent for the range of values. MDRD vs CKD EPI (Scr) had the highest accuracy in comparison to other compared formula. The performance between MDRD versus CKD EPI (Scys) was different. There was a good agreement between MDRD and CKD EPI (Scr).in both stage 3 and stage 4 CKD. The MDRD vs CKD EPI (Scr) classified 72.2% of the patients correctly. CONCLUSIONS: In conclusion, there was a good agreement between CKD-EPI (Scr) and MDRD equations. CKD-EPI equation based on creatinine estimation is widely accepted method and clinicians may use this equation in routine clinical practice to assess kidney function among patients with type 2 diabetes.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".