FP687THE ASSOCIATION BETWEEN INTRA-DIALYTIC SYMPTOM CLUSTERS AND RECOVERY TIME IN PATIENTS UNDERGOING CHRONIC HEMODIALYSIS: AN EXPLORATORY ANALYSIS
Bibliographic record
Abstract
INTRODUCTION: Individuals receiving hemodialysis may experience symptoms during a hemodialysis treatment and freuqently report feeling unwell after dialysis. The time it takes to recover from a dialysis treatment is called the recovery time, an important aspect of quality of life. We explored the relationship between symptoms during dialysis, recovery time and health related quality of life. METHODS: WE conducted a prospective observational study of 118 prevalent hemodialysis patients in two Canadian centers. Participants were asked to report the degree to which they experienced 10 intra-dialytic symptoms at each dialysis treatment for a week and the time it took to recover from each dialysis treatment as well as health related quality of life (using the Kidney Disease and Quality-of-Life-Short Form [KDQoL-SF36]) for the week. Principal component analysis was used to identify clusters of inter-related intra-dialytic symptoms. Mixed-effects, ordinal regression was used to examine the association of symptom clusters and recovery time, and the mental component score (MCS) and physical component score (PCS) of the KDQoL-SF36. RESULTS: Principal component analysis identified two symptom clusters explaining 39% of the total variance. The first cluster included symtoms of nervousness, lack of energy, muscle cramps, back pain and muscle soreness, whereas the second cluster included nausea and vomiting, diarrhea, chest pain and headaches. After adjusting for patient characteristics, the first cluster of symptoms was significantly associated with longer post-dialysis recovery time (OR=1.72; 95% CI=1.29, 2.30). The first cluster of symptoms was also significantly associated with decreased MCS and PCS scores. The second cluster of symptoms was not significantly associated with post-dialysis recovery time (OR=1.27; 95% CI=0.97, 1.64), or quality of life scores. CONCLUSIONS: Intra-dialytic symptoms are correlated and may share a common cause. The presence of certain symptoms affect recovery time and health related quality of life and interventions to alleviate the causes of these intra-dialytic symptoms may improve the lives of patients receiving hemodialysis.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".