#2838 Dialysis regret is associated with level of information about health status but not with frailty
Bibliographic record
Abstract
Abstract Background and Aims Elderly patients (>75 years of age) are the fastest growing group on dialysis with a high burden of multi-morbidity, functional dependence and frailty. Treatment decisions about End-Stage Renal Disease (ESRD) in this group are complex and quality of life, development of functional status and independence are often given higher priority than prolongation of life. Studies from the U.S., Canada and the Netherlands have shown varying degrees of dialysis regret (7–61%) [1–3]. As frailty is common among patients on dialysis and associated with poor clinical outcomes, the aim of this study was to determine the prevalence of regret in having started dialysis and associated risk factors. Method This cross-sectional, multicenter, prospective study in southern Germany included 123 adult patients on maintenance dialysis. Dialysis regret was assessed by a single question as part of a 6-item questionnaire. Frailty status was assessed based on modified Fried criteria and by self-assessment using a 5-point visual analogue scale (VAS). Statistical analysis was performed using Fisher's exact test and Chi-square test. Results The median age was 68 years, 71% were male, 40% of the participants were considered frail using the Fried criteria. Only 7/123 (5.7%) patients reported regretting the decision to have started dialysis (Table 1). 85% of patients reported sufficient information about their own health status. We found a significant correlation between the level of information regarding own health status and dialysis regret. There was no correlation of dialysis regret with age, sex, frailty or dialysis vintage. Even though frailty status was not correlated with dialysis regret, frail patients were significantly less informed about their own health status. Of note, 82% of patients reported that the decision to initiate dialysis was driven mainly by the health care team or their family (Fig. 1). The main reasons for starting dialysis were prolongation of life and the feeling of having no other choice (Fig. 2). Conclusion Dialysis regret was uncommon in our cohort of elderly dialysis patients and was associated with the level of information about health status but not with age, sex, frailty status, comorbid conditions or dialysis vintage. This underscores the importance of patient-education and the process of shared-decision making.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".