Shared Decision-Making for a Dialysis Modality
Bibliographic record
Abstract
The prevalence of kidney failure continues to rise globally. Dialysis is a treatment option for individuals with kidney failure; after the decision to initiate dialysis has been made, it is critical to involve individuals in the decision on which dialysis modality to choose. This review, based on evidence arising from the literature, examines the role of shared decision-making (SDM) in helping those with kidney failure to select a dialysis modality. SDM was found to lead to more people with kidney failure feeling satisfied with their choice of dialysis modality. Individuals with kidney failure must be cognizant that SDM is an active and iterative process, and their participation is essential for success in empowering them to make decisions on dialysis modality. The educational components of SDM must be easy to understand, high quality, unbiased, up to date, and targeted to the linguistic, educational, and cultural needs of the individual. All individuals with kidney failure should be encouraged to participate in SDM and should be involved in the design and implementation of SDM approaches.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".