THE IMPACT OF ESTIMATED GLOMERULAR FILTRATION RATEREPORTING ON NEPHROLOGY REFERRAL PATTERN, PATIENT CHARACTERISTICS AND OUTCOME
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is a growing public health problem worldwide. The key to reducing the burden of CKD is earlydetection and delay of disease progression. An elevated serum creatinine concentration, [Cr], is acommon indicator of CKD. However, even with advanced CKD, [Cr] may not be highamong patients with low muscle mass (particularly the elderly). Thus, the use of the estimated glomerular filtration rate (eGFR) has been advocated for identifying severe occult kidney disease. In January 2006, community laboratories in Ontario, Canada, began to report an eGFR value along with every [Cr] result. The present study sought to investigate the impact of eGFR reporting on nephrology referrals and subsequent patient outcome. Methods: The current study consisted of a retrospective analysis of all referrals to the adult general nephrology clinics at Sunnybrook Health Sciences Centre 15 months before and after eGFR reporting took effect on January 1, 2006. Results: eGFR reporting was associated with a significant rise in the number of referrals (971 to 1101, p=0.03), a 51% rise in patient wait-time (from 73 days to 110days, p < 0.001) and an increase in nephrologists’ workload. Patients referred after eGFR reporting were older, but suffered from fewer comorbidities such as hypertension, vascular disease, and dementia. There was an increase in the number of patients referred with stage 3 CKD, but no change in the proportion of stage 4 and 5 CKD referrals or the number of patients initiating dialysis. 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 ESRD frequency. Screening programs may need to be implemented along with knowledge translation strategies in order to achievethe goal of preventing and delaying progression of CKD. Reference: Canadian Society of Nephrology, September 2006. Careand referral of adult patients with reduced kidney function- Position paperfrom the Canadian Society of Nephrology.Available at: http://www.csnscn.ca/local/files/CSN-Documents/CSN%20Postion%20Paper%20Sept2006.pdf
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".