Evaluation of GFR Estimating Equations in the General Community
Bibliographic record
Abstract
The Kidney Disease Outcomes Quality Initiative has recommended the use of GFR estimating equations to detect silent chronic kidney disease (CKD) in the community. The benefit of general reporting of CKD must be balanced with the harm of mislabeling people who do not have CKD. The popular Cockcroft-Gault (CG) and Modification of Diet in Renal Disease (MDRD) GFR estimating equations were compared with the recently devised Rule equation in a representative community population sample (2166) divided into subsamples with (385) and without (1781) previous renal impairment. The prevalence of CKD was CG > MDRD >> Rule estimates. The magnitude of difference in prevalence of CKD as detected by the MDRD and CG versus the Rule equation increases markedly when the subsamples with (30.8 and 29.7 versus 17.5%) and without (12 and 11.3 versus 3.0%) previous kidney impairment are compared. General demographic and potential or known risk factors were used in a logistic regression model to assess the association with CKD. The MDRD estimates note female gender (odds ratio 2.19; 95% confidence interval 1.63 to 2.95) and both MDRD and the Rule equations identify hypertension and diabetes as significant CKD risk factors. All estimating equations identify age to be associated with CKD. The annualized serial decline in GFR was CG > MDRD > Rule estimates. Only the Rule GFR estimates detected a greater decline in renal impaired versus unimpaired populations. The calibrated Rule equation seems to perform better than CG and MDRD (CKD 3 versus 11.3 to 12%) but lacks validation against gold standards for community-based screening.
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 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 | 0.091 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".