Table 5_Trends in neurology medical education: a bibliometric analysis (2000–2023).xlsx
Bibliographic record
Abstract
Background The development of neurology closely correlates with improvements in medical education, which provides essential knowledge and skills to tackle the growing global challenge of neurological disorders. This study aimed to perform a bibliometric analysis to assess the key areas and trends concerning the interface between neurology and medical education for the period spanning from 2000 to 2023. Objective This study aimed to perform a bibliometric analysis to assess the key areas and trends concerning the interface between neurology and medical education for the period spanning from 2000 to 2023. Methods We gathered articles from the Web of Science Core Collection database and employed two bibliometric tools, CiteSpace and VOSviewer, to evaluate and quantify various impact and collaboration metrics. Our analysis included annual publication data, journals, co-cited journals, countries/regions, institutions, authors, and co-cited authors. Furthermore, we identified emerging research areas linked to neurology and medical education by investigating the co-occurrence and bursts of keywords and co-cited references. Results From 2000 to 2023, a total of 900 articles investigating the correlation between neurology and medical education were published in 297 academic journals. These articles were authored by 4,399 researchers from 893 institutions across 92 countries/regions. The United States, England, and Canada emerged as the leading countries in this field, with the United States maintaining a dominant position. Harvard University was identified as the most productive institution. Gilbert Donald L emerged as the top author, while Jozefowicz Rf recorded the highest number of co-citations. The journal Neurology was not only the most prolific in publishing articles at the intersection of neurology and medical education but was also the journal that received the most co-citations. The main themes of these articles centered around psychology, education, social health, nursing, and medicine, with keywords frequently relating to education, students, and neurological disorders. Conclusion Neurophobia within neurology medical education remains a significant area of research, contributing to a deeper understanding of the relationship between neurology and medical training. Emerging areas such as resident education, medical education training, developmental neurology, and parental involvement may offer valuable guidance and new insights for further research in the field of neurology education.
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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: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | 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 | 0.001 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.660 | 0.903 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.981 | 0.108 |
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".