Prevalence and Persistence of Uremic Symptoms in Incident Dialysis Patients
Bibliographic record
Abstract
Background Uremic symptoms are major contributors to the poor quality of life among patients on dialysis, but whether their prevalence or intensity has changed over time is unknown. Methods We examined responses to validated questionnaires in two incident dialysis cohort studies, the Choices for Health Outcomes in Caring for ESRD (CHOICE) study ( N =926, 1995–1998) and the Longitudinal United States/Canada Incident Dialysis (LUCID) study ( N =428, 2011–2017). We determined the prevalence and severity of uremic symptoms—anorexia, nausea/vomiting, pruritus, sleepiness, difficulty concentrating, fatigue, and pain—in both cohorts. Results In CHOICE and LUCID, respectively, mean age of the participants was 58 and 60 years, 53% and 60% were male, and 28% and 32% were black. In both cohorts, 54% of the participants had diabetes. Median time from dialysis initiation to the symptoms questionnaires was 45 days for CHOICE and 77 days for LUCID. Uremic symptom prevalence in CHOICE did not change from baseline to 1-year follow-up and was similar across CHOICE and LUCID. Baseline symptom prevalence in CHOICE and LUCID was as follows: anorexia (44%, 44%, respectively), nausea/vomiting (36%, 43%), pruritus (72%, 63%), sleepiness (86%, 68%), difficulty concentrating (55%, 57%), fatigue (89%, 77%), and pain (82%, 79%). In both cohorts, >80% of patients had three or more symptoms and >50% had five or more symptoms. The correlation between individual symptoms was low ( ρ <0.5 for all comparisons). In CHOICE, no clinical or laboratory parameter was strongly associated with multiple symptoms. Conclusions The burden of uremic symptoms among patients on dialysis is substantial and has not changed in the past 15 years. Improving quality of life will require identification of the factors that underlie the pathogenesis of uremic symptoms and better ways of removing the toxins that are responsible.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".