Recognition of CKD After the Introduction of Automated Reporting of Estimated GFR in the Veterans Health Administration
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Early detection of CKD is important for slowing progression to renal failure and preventing cardiovascular events. Automated laboratory reporting of estimated GFR (eGFR) has been introduced in many health systems to improve CKD recognition, but its effect in large, United States-based health systems remains unclear. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using Veterans Affairs (VA) laboratory and administrative data, two nonoverlapping national cohorts of patients receiving care in VA medical centers before (n=66,323) and after (n=16,670) implementation of automated eGFR reporting between 2004 and 2010 were identified. Recognition was assessed by the presence of new CKD diagnostic codes, use of additional diagnostic testing, outpatient nephrology visits, or overall CKD recognition (receipt of at least one of these outcomes) for each patient during the 12-month period after their first eligible creatinine or eGFR laboratory result. Generalized estimating equations were used to assess change before and after automated eGFR reporting. RESULTS: Overall CKD recognition increased from 22.1% of veterans before eGFR reporting to 27.5% in the post-eGFR reporting period (odds ratio [OR], 1.19; 95% CI, 1.12 to 1.27; P<0.001). Higher overall CKD recognition was driven largely by increased documentation of CKD diagnosis codes in medical records (OR, 1.31; 95% CI, 1.21 to 1.41; P<0.001) and diagnostic testing for CKD (OR, 1.13; 95% CI, 1.03 to 1.24; P<0.01) rather than outpatient nephrology consultation. Automated eGFR reporting was not associated with greater CKD recognition among black or older patients (P=0.07). CONCLUSIONS: Automated eGFR laboratory reporting improved documentation of CKD diagnoses but had no effect on nephrology consultation. These findings suggest that to advance CKD care, further strategies are needed to ensure appropriate follow-up evaluation to confirm and effectively evaluate CKD.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".