Patient-reported advantages and disadvantages of peritoneal dialysis: results from the PDOPPS
Bibliographic record
Abstract
BACKGROUND: Patient-reported measures are increasingly recognized as important predictors of clinical outcomes in peritoneal dialysis (PD). We sought to understand associations between patient-reported perceptions of the advantages and disadvantages of PD and clinical outcomes. METHODS: In this cohort study, 2760 PD patients in the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS) completed a questionnaire on their PD experience, between 2014 and 2017. In this questionnaire, PDOPPS patients rated 17 aspects of their PD experience on a 5-category ordinal scale, with responses scored from - 2 (major disadvantage) to + 2 (major advantage). An advantage/disadvantage score (ADS) was computed for each patient by averaging their response scores. The ADS, along with each of these 17 aspects, were used as exposures. Outcomes included mortality, transition to hemodialysis (HD), patient-reported quality of life (QOL), and depression. Cox regression was used to estimate associations between ADS and mortality, transition to HD, and a composite of the two. Logistic regression with generalized estimating equations was used to estimate cross-sectional associations of ADS with QOL and depression. RESULTS: While 7% of PD patients had an ADS < 0 (negative perception of PD), 59% had an ADS between 0 and < 1 (positive perception), and 34% had an ADS ≥1 (very positive perception). Minimal association was observed between mortality and the ADS. Compared with a very positive perception, patients with a negative perception had a higher transition rate to HD (hazard ratio [HR] = 1.67; 95% confidence interval [CI]: 1.21, 2.30). Among individual items, "space taken up by PD supplies" was commonly rated as a disadvantage and had the strongest association with transition to HD (HR = 1.28; 95% CI 1.07, 1.53). Lower ADS was strongly associated with worse QOL rating and greater depressive symptoms. CONCLUSIONS: Although patients reported a generally favorable perception of PD, patient-reported disadvantages were associated with transition to HD, lower QOL, and depression. Strategies addressing these disadvantages, in particular reducing solution storage space, may improve patient outcomes and the experience of PD.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".