[Gabapentin in the treatment of neuropathic pain in patients with type 2 diabetes mellitus].
Bibliographic record
Abstract
STUDY AIM: Assessment of pain relief in type 2 diabetes mellitus patients with neuropathic pain treated with gabapentin at daily dose 2400 mg. MATERIAL AND METHODS: 26 patients with type 2 diabetes mellitus and painful neuropathy were included into the study. The duration of pain symptoms ranged from 1 to 3.5 years (mean 1.9 +/- 0.6 year). Pain intensity was assessed using Short-Form McGill Pain Questionnaire (SFMPQ), visual analogue scale (VAS), and Present Pain Intensity Scale (PPI) before the study and after 6-week treatment with gabapentin. The sensory and motor conduction studies, electromyography from tibial anterior muscle and skin sympathetic response were recorded in the patients and compared with the results obtained from the control group. RESULTS: The neurophysiological examinations carried out in the patients with sensory neuropathy demonstrated a significant conduction impairment in sensory and motor nerves as well as in sympathetic sweat-secreting nerves. After six weeks of gabapentin treatment in 2400 mg daily dose a significant pain reduction was observed, assessed by means of SF-MPQ, VAS and PPI questionnaires. The authors have demonstrated a significant analgesic effect of gabapentin in patients with diabetic neuropathy and the suggest administration of this preparation in chronic diabetic neuropathy in order to improve the quality of patients' life.
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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".