Research trends in esketamine for depression over the past decade: a bibliometric analysis
Bibliographic record
Abstract
Background: Patients suffering from depression frequently encounter extended periods of low moods and lack of enjoyment or enthusiasm for activities. It leads to suicidal thoughts and presents a potential hazard to their safety. Nowadays, there has been significant progress in researching the effectiveness and safety of esketamine in treating depression. Hence, this paper employs bibliometric analysis to investigate the evolution and future research trajectories of this domain. Methods: We utilize Excel, VOSviewer, and CiteSpace software to generate bibliometric network visualizations to analyze, construct, and quantitatively evaluate pertinent literature, which facilitates a lucid and intuitive presentation of the trends and frontiers in this research domain. Results: Annual publications increased from 2015 to 2024, totaling 925 articles, with 286 studies published in 2024. The USA published the most papers (n=308), followed by China (n=260) and Canada (n=114). Three of the top journals were Journal of Affective Disorders (n=56,IF=4.90), Frontiers in Psychiatry (n=38,IF=5.44), and International Journal of Neuropsychopharmacology (n=21,IF=4.50). The most published authors were McIntyre, Roger S (n=52), followed by Hashimoto, Kenji (n=49), Rosenblat, Joshua D (n=41). The keywords that have been relevant to the topic for the last decade are "treatment-resistant depression", "efficacy", "antidepressant" and "suicidal ideation". Conclusions: This bibliometric analysis showed a significant increase in research on the use of esketamine in the treatment of depression. The main focus of current research is still the assessment of long-term use safety. In addition, the huge difference in research resources between developed countries and low- and middle-income countries remains an unresolved issue.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.298 | 0.341 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".