Characteristics of outpatients referred for a first consultation with a nephrologist: impact of different guidelines
Bibliographic record
Abstract
Chronic kidney disease (CKD) affects > 10% of the population but not all CKD patients require referral to a nephrologist. Various recommendations for referral to nephrologists are proposed worldwide. We examined the profile of French patients consulting a nephrologist for the first time and compared these characteristics with the recommendations of the International Kidney Disease: Improving Global Outcomes (KDIGO), the French “Haute Autorité de Santé” (HAS), and the Canadian Kidney Failure Risk Equation (KFRE). University Hospital electronic medical records were used to study patients referred for consultation with a nephrologist for the first time from 2016 to 2018. Patient characteristics (age, sex, diabetic status, estimated glomerular filtration rate (eGFR) and urine protein-to-creatinine ratio (PCR), etiology reported by the nephrologist) and 1-year patient follow-up were analyzed and compared with the KDIGO, HAS and Canadian-KFRE recommendations for referral to a nephrologist. The stages were defined according to the KDIGO classification, based upon kidney function and proteinuria. The 1,547 included patients had a median age of 71 [61–79] years with 56% males and 37% with diabetes. The main nephropathies were vascular (40%) and glomerular (20%). The KDIGO classification revealed 30%, 47%, 19%, 4% stages G1-2 to G5, and 50%, 22%, 28% stages A1-A3, respectively. According to KDIGO, HAS and KFRE scores, nephrologist referral was indicated for 42%, 57% and 80% of patients respectively, with poor agreement between recommendations. Furthermore, we observed 890 (57%) patients with an eGFR> 30 ml/min and a urine protein to creatinine ratio 0.5 g/g, mostly aged over 65 years (67%); 40% were diabetic, and 57% had a eGFR > 45 ml/min/1.73m 2 , 56% were diagnosed as vascular nephropathy and 11% with unknown nephropathy. These results underline the importance of better identifying patients for referral to a nephrologist and informing general practitioners. Other referral criteria (age and etiology of the nephropathy) are debatable.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".