Online clinical pathway for chronic kidney disease management in primary care: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Clinical 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. METHODS: . 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".