Detection of Chronic Kidney Disease With Laboratory Reporting of Estimated Glomerular Filtration Rate and an Educational Program
Bibliographic record
Abstract
BACKGROUND: Serum creatinine concentration is an inadequate screening test for chronic kidney disease, especially in elderly patients. We hypothesized that laboratory reporting of estimated glomerular filtration rate (GFR) accompanied with an educational intervention would improve recognition of chronic kidney disease (CKD). METHODS: We conducted a before-and-after study at an outpatient family medicine practice. Patients 65 years or older for whom a Cockcroft-Gault GFR could be calculated from their medical record were included. The intervention consisted of automatic reporting of estimated GFR by the hospital laboratory along with an educational intervention directed toward the primary care physicians. The primary outcome was the recognition of CKD (defined as a Cockroft-Gault GFR <60 mL/min [<1.0 mL/s]) by the primary care physician. Factors associated with the recognition of CKD were also determined. RESULTS: The study population comprised 324 patients. Prior to the study intervention, 22.4% of patients with CKD were recognized, which increased to 85.1% after the intervention. Before the intervention, recognition was more likely in male subjects (odds ratio, 4.3; 95% confidence interval, 1.9-9.8) and patients with diabetes (odds ratio, 3.4; 95% confidence interval, 1.6-7.6). These associations were no longer statistically significant after the intervention. CONCLUSION: Laboratory reporting of estimated GFR coupled with an educational program markedly improves the recognition of CKD in the primary care setting.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".