The Impact of Estimated Glomerular Filtration Rate Reporting on Nephrology Referral Pattern, Patient Characteristics and Outcome
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a growing public health problem worldwide. The estimated glomerular filtration rate (eGFR) has been advocated as a means to detect CKD. In January 2006, community laboratories in Ontario, Canada, began to report eGFR values along with every serum creatinine result. The present study sought to investigate the impact of eGFR reporting on nephrology referrals and patient outcome. METHODS: We conducted a retrospective analysis of referrals to an adult general nephrology clinic 24 months before and after eGFR reporting took effect. RESULTS: eGFR reporting was associated with a significant rise in the number of referrals (1,330-1,496, p = 0.009), a 33% rise in patient waiting time (from 75 to 100 days, p < 0.001), and an increase in nephrologists' workload. Patients referred after eGFR reporting were older, but suffered from fewer comorbidities such as hypertension and vascular disease. There was an increase in the number of patients referred with stage 3 CKD, but a drop in the proportion of stage 4 and 5 CKD referrals and no change in time to renal replacement therapy. CONCLUSION: Laboratory reporting of eGFR increased nephrology referral volume, patient waiting times, and nephrologists' workload, without a demonstrable benefit in terms of detection and referral of severe (stage 4 and 5) CKD, nor in the reduction of end-stage renal disease frequency.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".