Low Agreement between the Modified Diet and Renal Disease Formula and the Cockcroft-Gault Formula for Assessing Chronic Kidney Disease in Cognitively Impaired Elderly Outpatients
Bibliographic record
Abstract
OBJECTIVE: Chronic kidney disease is a global public health concern. Glomerular filtration rate (GFR) prediction based on serum creatinine is used to assess renal function in the elderly. The Cockcroft-Gault (CG) formula, based on body surface area (CG/BSA formula), and the Modified Diet and Renal Disease formula (MDRD formula) are commonly used in assessing renal function in clinical practice. The objective of this study was to investigate the agreement between the GFR estimate, CG/BSA formula, and the MDRD formula in elderly outpatients. METHODOLOGY: An outpatient chart review was conducted on consecutive elderly patients aged ≥ 65 years over a 9-month period. Data regarding age, gender, cognitive status, clock drawing, weight, height, and serum creatinine were collected. Pearson's correlation coefficient, Bland-Altman plot, and kappa statistics were used for statistical analysis. RESULTS: Of the 170 patients who participated in the study, 71% were cognitively impaired or had dementia. Using the CG/BSA formula, 79% of the patients had stage 3 renal disease (GFR< 60 mL/min); only 56% were diagnosed as such using the MDRD formula. There was a high correlation between the CG/BSA and MDRD formulas (Pearson correlation coefficient, 0.88; P < 0.0001). However, the kappa statistic was 0.47, indicating low agreement between the 2 formulas. CONCLUSION: The diagnosis of having stage 3 chronic kidney disease depended on whether the CG/BSA or MDRD formula was used. A 17% discordance rate (9.61 mL/min) was seen between the MDRD and CG/BSA formulas for estimating GFR, and there was low agreement between the 2 formulas. Further studies are needed to assess which predictive formula is appropriate for elderly patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".