The Uremic Environment and Muscle Dysfunction in Man and Rat
Bibliographic record
Abstract
BACKGROUND: Patients reaching end-stage renal disease experience debilitating fatigue, with progression of this disease, rendering patients dysfunctional in their everyday lives. METHODS: In vivo measurements of muscle function, assessed using surface electromyography (EMG), were made on 25 patients prior to and after a session of hemodialysis (HD) treatment, along side in vitro measurements of muscle function in isolated rat muscles incubated in normal or uremic conditions approximating to those found in uremic rats (rat uremic: RU) or uremic humans (human uremic: HU). RESULTS: HD significantly affected plasma values, e.g. reducing urea (69%), creatinine (62%), potassium (23%) and phosphate (48%) concentrations in patients (all p < 0.01). Treatment also improved the EMG frequency of 2nd dorsal interosseous (fast-twitch) (p < 0.01), although no change was noted for vastus lateralis (slow-twitch). In isolated rat muscles, a uremic environment had no significant effect on slow-twitch soleus during field stimulation, however, in fast-twitch extensor digitorum longus, a significant 23% (RU) and 22% (HU) faster rate of decline in force was measured, compared to controls (p < 0.001 and p < 0.01, respectively). CONCLUSION: It is concluded that (1) muscle weakness and its electrophysiological correlates may be rapidly induced by uremic solutes and rapidly reversed when the solutes are removed by dialysis, and (2) fast-twitch muscles are more readily affected by uremic conditions than slow-twitch muscles.
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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 | 0.000 |
| 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".