Post‐dialysis fatigue: Comparison of bicarbonate hemodialysis and online hemodiafiltration
Bibliographic record
Abstract
INTRODUCTION: The present cross-sectional study aimed to compare the prevalence, the characteristics of post-dialysis fatigue and the length of recovery time after hemodialysis in prevalent end-stage renal disease patients (ESRD) receiving bicarbonate hemodialysis (HD) or hemodiafiltration (HDF). METHODS: Patients were suffering from post-dialysis fatigue if they spontaneously offered this complaint when asked the open-ended question: "Do you feel fatigued after dialysis?". Moreover, each patient was invited to rate the intensity, duration, and frequency of post-dialysis fatigue from 1 to 5. In order to assess RECOVERY TIME AFTER DIALYSIS, patients were invited to answer to the following single open-ended question: "How long does it take you to recover from a dialysis session?" FINDINGS: We included 335 patients: 252 received HD and 83 received HDF. Post-dialysis fatigue was present in 204 patients (60.9%). Prevalence of post-dialysis fatigue did not differ significantly between patients on HD (62.3%) and on HDF (56.6%; p = 0.430). Median recovery time after dialysis was 180 min [180-240] and did not differ significantly between the two subgroups (180 min [130-240] and 240 min [120-332] p = 0.671, respectively). Median post-dialysis fatigue intensity, duration, and frequency were 3 [1-5], 3 [1-5], and 4 [1-5] and did not differ significantly between patients on HD and on HDF. At the multivariate analysis, age, ADL and hemoglobin levels were the independent predictors of the HDF treatment. DISCUSSION: Prevalence and characteristics of post-dialysis fatigue do not differ significantly between patients receiving bicarbonate HD or HDF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".