The Progression of Uremic Polyneuropathy in Patients on Hemodialysis and Hemofiltration: A Two‐Year Study
Bibliographic record
Abstract
Uremic polyneuropathy is one of the major complications of long-term end-stage renal disease. In the present study, we performed an electrophysiologic evaluation in 17 patients having a mean age of 49 ± 11 years. The patients were divided into two groups according to dialysis method. Group A included 9 patients who were undergoing conventional hemodialysis (mean age, 44.2 ± 12.5 years; mean duration on dialysis, 21.7 ± 4.3 months); group B included 8 patients undergoing hemofiltration (mean age, 55.2 ± 5.2 years; mean duration on treatment, 27 ± 7.6 months). Measurements of the distal latency time of the sensory fibers (median, ulnar, and sural nerves), and measurements of the distal latency time and peripheral conduction velocity of the motor fibers (median and peroneal nerves) were performed. In addition, we recorded somatosensory evoked potentials after peripheral stimulation of the median and peroneal nerves. The electrophysiologic evaluations were repeated two times at intervals of 12 months. In group A, a statistically significant worsening of motor and sensory conductance in the upper and lower limbs was observed; in group B, a statistically significant improvement was found. These findings suggest that hemofiltration has a more beneficial effect on motor and sensory conductivity than does conventional hemodialysis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".