Trends in Volume, Appropriateness, and Outcomes of Referrals to Nephrology over the Last Two Decades: A Retrospective Analysis Using the Alberta Kidney Disease Network Database
Bibliographic record
Abstract
Background: It is well-established that guideline-concordant referrals to nephrology are associated with improved patient outcomes. However, some referrals are unnecessary (guideline-discordant) leading to high volumes and delays for referrals that are guideline concordant. We investigated the trends in the number of referrals to nephrology, and related outcomes in Alberta. Methods: Retrospective cohort analysis of patients with at least one visit to a nephrologist from primary care between 2006 and 2019. A referral was considered appropriate based on the KDIGO defined criteria (estimated glomerular filtration rate (eGFR) < 30 mL/min per 1.73m2, albumin creatinine ratio (ACR) ≥ 30 mg/mmol or protein creatinine ratio ≥ 50 mg/mmol, or Urine dipstick ≥ 2+ protein on two consecutive measurements, and/or eGFR persistently declined ≥ 5 mL/min per 1.73m2 from the first eGFR measurement). Results: Of 69,372 patients (mean age 62.5; 50.7% female), only 28,518 (41.1%) referrals met criteria as guideline-concordant (Figure 1A). Patients referred in a guideline-concordant manner were significantly more likely to be older, men, and with comorbid conditions (diabetes, hypertension, and cardiovascular disease). There has been an increasing trend in the number of guideline concordant and discordant referrals from 2006 to 2019 (Figure 1B). Patients who met guideline-criteria for referrals were likely to be prescribed renoprotective medications but more likely to experience clinical outcomes of kidney failure, cardiovascular events, and all-cause mortality. Conclusions: The number of referrals to nephrology from primary care continues to increase, and a large proportion of these referrals were guideline discordant. Interventions targeted to primary care at reducing the number of non-guideline concordant referrals are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".