The effects of exercise during hemodialysis on adequacy
Bibliographic record
Abstract
Pedalling during hemodialysis (HD) has been shown to increase solute clearance in a previous study. In the present study, we aimed to test whether an easy to perform exercise program, not requiring a special device, could yield similar outcomes. Fifteen HD patients with the mean age of 48.4 ± 3.8 years were enrolled into the study. Patients with significant access recirculation (>10%), moderate to severe coronary artery disease, moderate to severe heart failure, severe chronic obstructive lung disease, and history of lower extremity surgery during last three month period were excluded. All patients were studied on two consecutive HD sessions with identical prescriptions. At the first session, standard HD was applied without exercise, whereas in the second session lower extremity exercise of 30 minutes duration was added. Reduction rates and rebound for urea, creatinine, and potassium and Kt/V were calculated. Wilcoxon signed rank test was applied in analysis and p < 0.05 was accepted as significance level. All patients completed the study. When both sessions were compared, mean arterial blood pressure (97 ± 3 mmHg vs 120 ± 4 mmHg, p < 0.001) and heart rate (77 ± 1 beats/min vs 92 ± 3 beats/min, p < 0.001) were higher in the exercise group. On the other hand, urea reduction rates, rebound values of urea, creatinine, and potassium were similar in both groups. Conclusion: In the study, we did not observe any changes in solute rebound and clearance with the exercise. Shorter duration of the exercise may be the explanation of failure to achieve desired outcomes. Increasing patients’ tolerance and fitness levels by means of steadily increasing exercise programs may be of help. Additionally, mode of exercise may also be responsible for different outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".