Impact of estimated glomerular filtration rate reporting on nephrology referrals: a review of the literature
Bibliographic record
Abstract
PURPOSE OF REVIEW: Estimated glomerular filtration rate (eGFR) reporting has been implemented by laboratories and jurisdictions around the world. The purpose of this review is to summarize the recent literature evaluating the association between eGFR reporting and outcomes, specifically assessing its impact on nephrology referrals and characteristics of the referred population. RECENT FINDINGS: Eight studies have evaluated the association between eGFR reporting and nephrology referrals, all published within the last 6 years. These studies consistently show an increase in referrals and referral rates following eGFR reporting. This increase in nephrology referrals was predominantly seen in women and the elderly. An increased referral rate and increased recognition of chronic kidney disease were noted amongst patients with stage 3 or higher kidney disease. Whether eGFR reporting results in an increase in 'inappropriate' referrals and increased resource use has been poorly studied. SUMMARY: Reporting of eGFR is associated with an increase in nephrology referrals, particularly among women and the elderly. Whether eGFR reporting is associated with improved patient outcomes remains to be determined.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".