Screening and Diagnosis of Chronic Kidney Disease in Adults Living With Diabetes: A Retrospective Cohort Study Using the Canadian Primary Care Sentinel Surveillance Network
Bibliographic record
Abstract
OBJECTIVES: In Canada, regional evaluations of screening practices for chronic kidney disease (CKD) among people with diabetes highlight areas for improvement; however, national estimates are notably absent. Estimates of CKD incidence often discount the expected decline in estimated glomerular filtration rate (eGFR) associated with age; age-adaptive thresholds may help account for this. We describe the frequency of screening and diagnosis of CKD among adults with diabetes from a nationally representative primary care cohort. METHODS: In this retrospective cohort study, we used electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network. We followed adult patients (≥18 years of age) with diabetes without CKD at baseline for 5 years starting in 2014. We determined the frequency of urine albumin-to-creatinine ratio (uACr) and/or eGFR testing over time. We identified incident CKD diagnoses based on eGFR measurements using fixed-threshold and age-adaptive definitions and quantified the incidence proportion and rate. RESULTS: We analyzed records from 37,604 patients with diabetes. Only 13% of patients had yearly eGFR and uACr testing for CKD, although roughly 60% had non-yearly use of both tests in 5 years. eGFR testing was performed more frequently than uACr testing (94.1% vs 76.6% having testing over follow-up). We found increased incidence proportions (14.6% vs 6.0%) and rates (33.1 vs 13.4 diagnoses per 1,000 person-years) of CKD using the fixed-threshold compared with age-adaptive definition. CONCLUSIONS: Our study presents the first national understanding of screening practices for CKD among people with diabetes in Canada. Specifically, increased use of uACr testing should be encouraged for early detection of changes in kidney function.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".