An opportunity for improved engagement and transparency: A systematic review of renal dialysis cost effectiveness and discrete choice experiment studies
Bibliographic record
Abstract
Much attention is given to patient and provider engagement, cost, and quality. Nephrology is in a unique position to examine the intersection of these issues given kidney dialysis is delivered at a high cost to chronically ill patients. Annual dialysis treatments in Canada range from $56,000-$107,000 per patient dependent on modality. Economists quantify the preferred modality by calculating cost effectiveness through quality-adjusted life years or determining utilization through Discrete Choice Experiments (DCEs). Cost-effectiveness studies identify peritoneal dialysis as the most economical, yet it is the least used. Discrete choice experiments address patient preferences but rarely include cost attributes. This presents a unique paradigm: cost studies do not include patient or physician perspectives, and DCEs do not consider cost. This systematic review of dialysis cost-effectiveness studies and DCEs identifies an opportunity to increase engagement and transparency by involving all care partners in assessing quality and cost.
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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 | 0.055 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".