Research hotspots and trends on neuropathic pain-related mood disorders: a bibliometric analysis from 2003 to 2023
Bibliographic record
Abstract
Introduction: Neuropathic Pain (NP) is often accompanied by mood disorders, which seriously affect the quality of life of patients. This study aimed to analyze the hotspots and trends in NP-related mood disorder research using bibliometric methods and to provide valuable predictions for future research in this field. Methods: Articles and review articles on NP-related mood disorders published from January 2003 to May 2023 were retrieved from the Web of Science Core Collection. We used CiteSpace to analyze publications, countries, institutions, authors, cited authors, journals, cited journals, references, cited references, and keywords. We also analyzed collaborative network maps and co-occurrence network maps. Results: A total of 4,540 studies were collected for analysis. The number of publications concerning NP-related mood disorders every year shows an upward trend. The United States was a major contributor in this field. The University of Toronto was the most productive core institution. C GHELARDINI was the most prolific author, and RH DWORKIN was the most frequently cited author. PAIN was identified as the journal with the highest productivity and citation rate. The current research hotspots mainly included quality of life, efficacy, double-blind methodology, gabapentin, pregabalin, postherpetic neuralgia, and central sensitization. The frontiers in research mainly focused on the mechanisms associated with microglia activation, oxidative stress, neuroinflammation, and NP-related mood disorders. Discussion: In conclusion, the present study provided insight into the current state and trends in NP-related mood disorder research over the past 20 years. Consequently, researchers will be able to identify new perspectives on potential collaborators and cooperative institutions, hot topics, and research frontiers in this field.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.111 | 0.165 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".