Evaluation of the Effect of Gabapentin in the Management of Uremic Pruritus in Hemodialysis Patients
Bibliographic record
Abstract
Background: Gabapentin is an antiepileptic agent that has analgesic properties in neuropathic pain. Given that few studies have assessed the effect of the low dose of gabapentin on uremic pruritus, this study aimed to evaluate the effect of gabapentin on pruritus of hemodialysis patients. Materials and Methods: This clinical trial study was conducted on dialysis patients who were referred to Shafa Hospital, Kerman. In this regard, 40 patients consumed 100 mg of gabapentin for one week. Then patients did not take any medication within the washout period and consumed a 100 mg placebo for one week. Assessment of pruritus severity was done by visual analog scale (VAS). Hematocrit, calcium, phosphor, creatinine, and albumin were evaluated. These measurements were done before and after treatments with placebo and gabapentin. Results: The main places of pruritus location in dialysis patients were the back (90 %), abdomen (80%), shoulder (80%), and head (70%). The mean pruritus severity before treatment, and after treatment with placebo and gabapentin, was 8.3± 1.5, 6.73 ± 1.17, and 4.58 ± 1.50, respectively. Significant difference was seen before and after treatment, in terms of pruritus severity (p<0.1). In addition, there was a significant difference between gabapentin and placebo groups, regarding the severity of pruritus (p<0.1). No significant difference was seen before and after treatment, regarding biochemical parameters (p>0.05). Conclusion: According to the findings, it seems that gabapentin can be an effective and safe treatment for pruritus in patients on hemodialysis. The therapeutic approach chosen for these patients is based on the neuropathic hypothesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".