Online Clinical Pathway for Chronic Kidney Disease Management in Primary Care: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract BackgroundClinical pathways aim to improve patient care. We sought to determine whether an online chronic kidney disease (CKD) clinical pathway was associated with improvements in CKD management. MethodsWe conducted a retrospective pre/post population-based cohort study using linked health data from Alberta, Canada. We included adults 18 years or older with mean estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m2. The primary outcome was measurement of an outpatient urine albumin creatinine ratio (ACR) in a 28-day period, among people without a test in the prior year. Secondary outcomes included use of guideline-recommended drug therapies (angiotensin-converting enzyme inhibitors, angiotensin receptor blockers and statins). Results The study period spanned October 2010 to March 2017. There were 84 independent 28-day periods (53 pre, 31 post pathway implementation) including 345,058 adults. The population was predominantly female (56%) with median age 77 years; most had category 3A CKD (67%) and hypertension (82%). In adjusted segmented regression models, the increase in the rate of change of ACR testing was greatest in Calgary zone (adjusted OR 1.19 per year, 95% CI 1.16-1.21), where dissemination of the pathway was strongest; this increase was more pronounced in those without diabetes (adjusted OR 1.25 per year, 95% CI 1.21–1.29). Small improvements in guideline-concordant medication use were also observed. Conclusions: Following implementation of an online CKD clinical pathway, improvements in ACR testing were evident in regions where the pathway was most actively used, particularly among individuals without diabetes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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".