FP321REGRET WITH THE DECISION TO START DIALYSIS IN OLDER PATIENTS: A DUTCH SURVEY AS QUALITY OF CARE INITIATIVE
Bibliographic record
Abstract
Introduction and Aims: Worldwide there is a rising influx of older patients with end stage renal disease (ESRD) starting with dialysis. In general, elderly patients prefer a treatment that focuses on quality of life rather than primarily on extending life. In a Canadian study 61% of 584 dialysis patients (mean age 68 year) regretted their decision to start dialysis (*Davison SN, 2010). In their study the decision making process reflected physicians and family members preferences rather than patient`s personal choice, which could be one of the reasons of the high regret rate. In the Netherlands multidisciplinary pre-dialysis education is formally established (NFN guidelines 2009) and patients are free to choose the modality which best suits their individual situation. However, it is unknown if Dutch dialysis patients, particularly older patients, regret their decision to initiate dialysis. Our primary objective was to measure the percentage of patients in the Netherlands who regret their decision to start dialysis and ascertain whether patients are satisfied with the treatment. Furthermore, we wanted to establish whether the factors age, gender, acute dialysis initiation and whose opinion was crucial in the decision making process, are related to regret.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".