Why do patients choose self-care dialysis?
Bibliographic record
Abstract
BACKGROUND: Self-care dialysis (SCD) is associated with cost savings and possibly improved quality of life compared to in-centre haemodialysis. Despite this, less than one in four dialysis patients are currently treated with SCD. Our objectives were to identify perceived advantages of SCD associated with increased odds of selecting SCD, and to evaluate the effect of an educational intervention on patients' perceptions of these advantages. METHODS: We randomized patients with GFR<30 ml/min to standard pre-dialysis education or a multifaceted educational intervention. After each component of the intervention, participants completed a questionnaire on which they identified advantages of SCD and indicated their preferred dialysis modality. We used inductive coding to identify perceived advantages of SCD. RESULTS: Seventy patients participated in the study. We identified three advantages of SCD: freedom, lifestyle and control. Of these, freedom and lifestyle were associated with increased odds of selecting SCD [OR 9.1 (2.0, 41.3) and 7.0 (1.6, 29.7), respectively]. We combined these advantages for receiver-operating characteristic (ROC) analysis to assess accuracy in predicting patients' intended choice of dialysis. The area under the ROC curve was 0.82 (0.70, 0.93). Patients who received the educational intervention were more likely to perceive freedom and control as advantages, and were less likely to identify no advantages of SCD. CONCLUSION: Patients who identify freedom and lifestyle advantages of SCD are more likely to choose SCD. Providing additional education on dialysis modalities increases patients' perceptions of the advantages of SCD and the odds of them selecting SCD.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".