Stimulated sweating as a therapy to reduce interdialytic weight gain and improve potassium balance in chronic hemodialysis patients: A pilot study
Bibliographic record
Abstract
Controlling the extracellular volume in hemodialysis patients is a difficult task. The aim of this study was to evaluate the capacity of different methods of stimulated sweating to reduce mean interdialytic weight gain (IWG), to improve blood pressure regulation, and potassium/urea balance. Two center, crossover pilot study. In Lausanne, hemodialysis patients took four hot-water baths a week, 30 minutes each, on nondialysis days during 1 month. In Sfax, patients visited the local Hammam Center four times a week. Hemodynamic parameters were recorded, and weekly laboratory analysis was performed. Results were compared with a preceding 1-month control period. In Lausanne, five patients (all men, median age 55 years) participated. Bathing temperature was (mean ± standard deviation) 41.2 ± 3°C and sweating-induced weight loss 600 ± 500 g. Mean IWG (control vs. intervention period) decreased from 2.3 ± 0.9 to 1.8 ± 1 kg (P = 0.004), Systolic blood pressure from 139 ± 21 to 136 ± 22 mmHg (P = 0.4), and diastolic blood pressure form 79 ± 12 to 75 ± 13 mmHg (P = 0.08); antihypertensive therapy could be reduced from 2.8 ± 0.4 to 1.9 ± 0.5 antihypertensive drugs per patient (P = 0.01). In Sfax (n = 9, median age 46 years), weight loss per Hammam session was 420 ± 100 g. No differences were found in IWG or BP, but predialysis serum potassium level decreased from 5.9 ± 0.8 to 5.5 ± 0.9 mmol/L (P = 0.04) and urea from 26.9 ± 6 to 23.1 ± 6 mmol/L (P = 0.02). Hot-water baths appear to be a safe way to reduce IWG in selected hemodialysis patients. Hammam visits reduce serum potassium and urea levels, but not IWG. More data in larger patient groups are necessary before definite conclusion can be drawn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".