Antiepileptic and Antidepressant Drugs in the Treatment of Neuropathic Pain and Depression of the Patients with Diabetic Neuropathy: A Comparative Study
Bibliographic record
Abstract
Objective: The aim of this study is to investigate the effects of two antidepressants; amitryptline, venlafaxine XR, and two anticonvulsants lamotrigine, oxcarbazepine on pain and depression in diabetic neuropathy (DN). Material and methods: Following the visual analog scale (VAS), Short-McGill pain questionnaire (SMPQ) and Beck depression inventory (BDI) performance, patients with diabetic neuropathy were randomly treated with lamotrigine (2x50 mg/day), oxcarbazepine (2x300 mg/day), amitryptiline (25 mg/day), or venlafaxine XR (75 mg/day). Patients presented to 6 follow-up visits, within 15 days of intervals. VAS scores were re-measured in each follow-up visits, whereas SMPQ and BDI scores were performed at the end of the study. Results: There were statistically significant differences between the BDI and SMPQ scores of the all study groups before treatment and at the last follow-up visit (p<0.05).Initial VAS scores were similar in the study groups, whereas VAS scores in the day 15, 30 and 90 revealed statistically significant differences in all groups (p<0.05). Lamotrigine and oxcarbazepine were found as effective as amitryptiline and venlafaxine XR on depression. Conclusion: Similar efficacity of antiepileptic and antidepressants in the precence of painful DN and depression is important to contribute in personalization of the treatments and to increase the therapeutic options. Key words: Diabetic Neuropathy; Depression; Oxcarbazepine; Lamotrigine; Venlafaxine; Amitriptyline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".