The Burden of Self-Dialysis: Opinion From Patient's Side
Bibliographic record
Abstract
Non-compliance is crucial in chronic diseases and increases with complexity of therapies. Objective and methods: to build and validate, in a pilot study involving home and self-limited hemodialysis (HD) patients, a questionnaire assessing dialysis related discomfort (12 items, 4 fields: support therapies, time requirements, relationship with hospital-machine, dialysis related discomfort) and the advantages of flexible schedules and simplified therapies (6 items). A 0–10 visual analogue scale was employed. Results. The study was completed by 50/52 (96%) patients on home or self-limited care HD in a satellite Unit: median age 50 yrs (24–80), follow-up 40 mths (2–360); therapy: 10 pills/day (2–24), 2 subcutaneous injections/week (2–30). The highest scores, describing discomfort, were recorded for: time requirements (median score 8/10), dependence upon the machine (7/10), needle punctures (6/10), drug therapy, vascular access and dependence upon others (5/10). The option of flexible schedules and simplified support therapies obtained maximal scores (median 10/10). No relationship among single scores, clinical data, dialysis or support therapy was found. According to the pattern of the answers, patients were grouped into three main profiles (attitude towards dialysis related problems): stoic (20 cases), suffering (11) and selective (19). No correlation with clinical data and profile was found. Conclusion. Support therapies and organizational aspects cause important discomfort to our dialysis patients. Further studies in different cohorts are needed to define them, to optimize treatment acceptance and compliance.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".