Does provision of a higher Kt/V<sub>urea</sub> make a difference? A hemodialysis controversial issue
Bibliographic record
Abstract
BACKGROUND: Adequate dialysis cannot be ascertained on the sole base of a normal or even a high Kt/V(urea) so the impetus of this study was to use the neurophysiologic studies as a marker of the biologic status of the hemodialysis patients to assess the optimum level of Kt/V(urea). METHODS: This study was carried out on 20 patients (15 men and 5 women) on maintenance hemodialysis; their ages ranged from 18 to 66 years. Initially, the patients were subjected to thorough clinical and laboratory investigations, and their dialysis adequacy was assessed by studying their urea kinetic modeling and neurophysiologic studies (Phase I). Dialysis was optimized to achieve a target Kt/V(urea) of 1.3 in Phase II and 1.5 in Phase III. The duration of each phase was six months at the end of which all patients were thoroughly reevaluated. Nutrition was not manipulated during the study. RESULTS: A neurophysiologic study showed a significant improvement of polyphasicity pattern of both proximal and distal muscles of the upper and lower limbs concomitant with improvement of quality of life on achieving a Kt/V(urea) of 1.5 (p < 0.001). There was no significant change of the duration and amplitude of all studied muscles, however. CONCLUSION: Achieving a Kt/V(urea) of 1.5 is a more suitable target for hemodialysis patients because it may be an avenue for improving the neuromuscular functions of these patients.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".