Trends in nephrology referral patterns for patients with chronic kidney disease: Retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Information on early, guideline discordant referrals in nephrology is limited. Our objective was to investigate trends in referral patterns to nephrology for patients with chronic kidney disease (CKD). METHODS: Retrospective cohort study of adults with ≥1 visits to a nephrologist from primary care with ≥1 serum creatinine and/or urine protein measurement <180 days before index nephrology visit, from 2006 and 2019 in Alberta, Canada. Guideline discordant referrals were those that did not meet ≥1 of: Estimated glomerular filtration rate (eGFR) ˂ 30 mL/min/1.73m2, persistent albuminuria (ACR ≥ 300 mg/g, PCR ≥ 500 mg/g, or Udip ≥ 2+), or progressive and persistent decline in eGFR until index nephrology visit (≥ 5 mL/min/1.73m2). RESULTS: Of 69,372 patients with CKD, 28,518 (41%) were referred in a guideline concordant manner. The overall rate of first outpatient visits to nephrology increased from 2006 to 2019, although guideline discordant referrals showed a greater increase (trend 21.9 per million population/year, 95% confidence interval 4.3, 39.4) versus guideline concordant referrals (trend 12.4 per million population/year, 95% confidence interval 5.7, 19.0). The guideline concordant cohort were more likely to be on renin-angiotensin system blockers or beta blockers (hazard ratio 1.14, 95% confidence interval 1.12, 1.16), and had a higher risk of CKD progression (hazard ratio 1.09, 95% confidence interval 1.06, 1.13), kidney failure (hazard ratio 7.65, 95% confidence interval 6.83, 8.56), cardiovascular event (hazard ratio 1.40, 95% confidence interval 1.35,1.45) and mortality (hazard ratio 1.58, 95% confidence interval 1.52, 1.63). CONCLUSIONS: A significant proportion nephrology referrals from primary care were not consistent with current guideline-recommended criteria for referral. Further work is needed to identify quality improvement initiatives aimed at enhancing referral patterns of patients with 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".