Prevention of hemodialysis‐related muscle cramps by intradialytic use of sequential compression devices: A report of four cases
Bibliographic record
Abstract
BACKGROUND: Hemodialysis (HD)-related lower extremity (LE) muscle cramps are a common cause of morbidity in end-stage renal disease patients on maintenance HD. Numerous pharmacologic and physical measures have been tried with variable success rates. METHODS: Sequential compression devices (SCD) improve venous return (VR) and are commonly used to prevent LE deep venous thrombosis in hospitals. We hypothesized that LE cramps are triggered by stagnant venous flow during HD and are preventable by improving VR. We prospectively studied four adult patients (mean age 61 +/- 14 years) on thrice-weekly HD who experienced two or more episodes of LE cramping weekly in the month before the study. SCD were applied before each HD on both legs and compressions were intermittently applied at 40 mmHg during treatment. RESULTS: All four patients reported complete resolution of cramping during the study period that lasted 1 month or 12 consecutive dialysis treatments. CONCLUSION: Application of SCD to LE may prevent the generation of LE HD-related cramping in a select group of patients. Larger, controlled studies are needed to establish the utility of this noninvasive alternative for the prevention of LE HD-related cramps.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".