A Cluster Randomized Trial of an Enhanced eGFR Prompt in Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite reporting estimated GFR (eGFR), use of evidence-based interventions in CKD remains suboptimal. This study sought to determine the effect of an enhanced eGFR laboratory prompt containing specific management recommendations, compared with standard eGFR reporting in CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A cluster randomized trial of a standard or enhanced eGFR laboratory prompt was performed in 93 primary care practices in Alberta, Canada. Although all adult patients with CKD (eGFR <60 ml/min per 1.73 m(2)) were included, medication data were only available for elderly patients (aged ≥66 years). The primary outcome, the proportion of patients with diabetes or proteinuria receiving an angiotensin converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB), was assessed in elderly CKD patients. RESULTS: There were 5444 elderly CKD patients with diabetes or proteinuria who were eligible for primary outcome assessment, irrespective of baseline ACEi/ARB use. ACEi/ARB use in the subsequent year was 77.1% and 76.9% in the standard and enhanced prompt groups, respectively. In the subgroup of elderly patients with an eGFR <30 ml/min per 1.73 m(2), ACEi/ARB use was higher in the enhanced prompt group. Among 22,092 CKD patients, there was no difference in the likelihood of a composite clinical outcome (death, ESRD, doubling of serum creatinine, or hospitalization for myocardial infarction, heart failure, or stroke) over a median of 2.1 years. CONCLUSIONS: In elderly patients with CKD and an indication for ACEi/ARB, an enhanced laboratory prompt did not increase use of these medications.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".