Cost-Effectiveness Analysis of a Randomized Trial Comparing Care Models for Chronic Kidney Disease
Bibliographic record
Abstract
Background and objectives Potential cost and effectiveness of a nephrologist/nurse–based multifaceted in-tervention for stage 3 to 4 chronic kidney disease are not known. This study examines the cost-effectiveness of a chronic disease management model for chronic kidney disease. Design, setting, participants, & measurements Cost and cost-effectiveness were prospectively gathered along-side a multicenter trial. The Canadian Prevention of Renal and Cardiovascular Endpoints Trial (CanPRE-VENT) randomized 236 patients to receive usual care (controls) and another 238 patients to multifaceted nurse/nephrologist–supported care that targeted factors associated with development of kidney and cardio-vascular disease (intervention). Cost and outcomes over 2 years were examined to determine the incremen-tal cost-effectiveness of the intervention. Base-case analysis included disease-related costs, and sensitivity analysis included all costs. Results Consideration of all costs produced statistically significant differences. A lower number of days in hospital explained most of the cost difference. For both base-case and sensitivity analyses with all costs in-cluded, the intervention group required fewer resources and had higher quality of life. The direction of the results was unchanged to inclusion of various types of costs, consideration of payer or societal perspective, changes to the discount rate, and levels of GFR. Conclusions The nephrologist/nurse–based multifaceted intervention represents good value for money be-cause it reduces costs without reducing quality of life for patients with chronic kidney disease.
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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 | 0.034 | 0.065 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".