Vortioxetine treatment for neuropathic pain in major depressive disorder: a three-month prospective study
Bibliographic record
Abstract
Introduction and objective: Several studies revealed the therapeutic potential of vortioxetine (Vo) for pain. In this context, we aimed to evaluate the efficacy of Vo as a safe and tolerable novel pharmacologic agent in treating neuropathic pain (NP) in patients with major depressive disorder (MDD). Materials and methods: The population of this cross-sectional prospective study consisted of all consecutive patients who were newly diagnosed with MDD by a neurology doctor at a psychiatric clinic and had NP for at least 6 months. All patients included in the sample were started on Vo treatment at 10 mg/day. They were assessed with Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Self-Reported Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS), Douleur Neuropathique 4 Questions (DN4), Montreal Cognitive Assessment (MoCA), and Neuropathic Pain Impact on Quality of Life (NePIQoL) at the beginning of treatment and during the follow visits conducted at the end of the first, second and third months of the treatment. During these follow-up visits, patients were also queried about any side effects of Vo. Results: The mean age of 50 patients included in the sample, 76% of whom were female, was 45.8 ± 11.2 years. There was a significant reduction in patients' NP complaints based on DN4 and S-LANNS, the subscales of NePIQoL, and significant improvement in MoCA. There was a significant reduction in patients' NP complaints based on DN4 and S-LANNS scores and a significant improvement in scores of the subscales of NePIQoL and MoCA. Conclusion: The study's findings indicate that Vo, with its multiple mechanisms of action, can effectively treat NP independently of its mood-stabilizing effect. Future indication studies for Vo are needed to establish Vo's efficacy in treating NP.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".