Factors influencing patient choice of dialysis versus conservative care to treat end-stage kidney disease
Bibliographic record
Abstract
BACKGROUND: For every patient with chronic kidney disease who undergoes renal-replacement therapy, there is one patient who undergoes conservative management of their disease. We aimed to determine the most important characteristics of dialysis and the trade-offs patients were willing to make in choosing dialysis instead of conservative care. METHODS: We conducted a discrete choice experiment involving adults with stage 3-5 chronic kidney disease from eight renal clinics in Australia. We assessed the influence of treatment characteristics (life expectancy, number of visits to the hospital per week, ability to travel, time spent undergoing dialysis [i.e., time spent attached to a dialysis machine per treatment, measured in hours], time of day at which treatment occurred, availability of subsidized transport and flexibility of the treatment schedule) on patients' preferences for dialysis versus conservative care. RESULTS: Of 151 patients invited to participate, 105 completed our survey. Patients were more likely to choose dialysis than conservative care if dialysis involved an increased average life expectancy (odds ratio [OR] 1.84, 95% confidence interval [CI] 1.57-2.15), if they were able to dialyse during the day or evening rather than during the day only (OR 8.95, 95% CI 4.46-17.97), and if subsidized transport was available (OR 1.55, 95% CI 1.24-1.95). Patients were less likely to choose dialysis over conservative care if an increase in the number of visits to hospital was required (OR 0.70, 95% CI 0.56-0.88) and if there were more restrictions on their ability to travel (OR=0.47, 95%CI 0.36-0.61). Patients were willing to forgo 7 months of life expectancy to reduce the number of required visits to hospital and 15 months of life expectancy to increase their ability to travel. INTERPRETATION: Patients approaching end-stage kidney disease are willing to trade considerable life expectancy to reduce the burden and restrictions imposed by dialysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".