Quality of Care for Patients With Chronic Kidney Disease in the Primary Care Setting: A Retrospective Cohort Study From Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Patients with chronic kidney disease may not be receiving recommended primary renal care. OBJECTIVE: To use recently established primary care quality indicators for chronic kidney disease to determine the proportion of patients receiving recommended renal care. DESIGN: Retrospective cohort study using administrative data with linked laboratory information. SETTING: The study was conducted in Ontario, Canada, from 2006 to 2012. PATIENTS: Patients over 40 years with chronic kidney disease or abnormal kidney function in primary care were included. MEASUREMENTS: In total, 11 quality indicators were assessed for chronic kidney disease identified through a Delphi panel in areas of screening, monitoring, drug prescribing, and laboratory monitoring after initiating an angiotensin converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB). METHODS: We calculated the proportion and cumulative incidence at the end of follow-up of patients meeting each indicator and stratified results by age, sex, cohort entry, and chronic kidney disease stage. RESULTS: Less than half of patients received follow-up tests after an initial abnormal kidney function result. Most patients with chronic kidney disease received regular monitoring of serum creatinine (91%), but urine albumin-to-creatinine monitoring was lower (70%). A total of 84% of patients age 66 and older did not receive a non-steroidal anti-inflammatory drug prescription of at least 2-week duration. Three quarters of patients age 66 and older were on an ACE inhibitor or ARB, and 96% did not receive an ACE inhibitor and ARB concurrently. Among patients 66 to 80 years of age with chronic kidney disease, 65% were on a statin. One quarter of patients age 66 and older who initiated an ACE inhibitor or ARB had their serum creatinine and potassium monitored within 7 to 30 days. LIMITATIONS: This study was limited to people in Ontario with linked laboratory information. CONCLUSIONS: There was generally strong performance across many of the quality of care indicators. Areas where more attention may be needed are laboratory testing to confirm initial abnormal kidney function test results and monitoring serum creatinine and potassium after initiating a new ACE inhibitor or ARB.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".