Looking backward toward the future: A bibliometric analysis of the last 40 years of meningioma global outcomes
Bibliographic record
Abstract
This study is the first comprehensive bibliometric analysis about meningioma to date. The aim of this study is to identify the most influential publications in this field through citation and co-citation analysis, to examine international collaborations, to identify the conceptual framework of the subject and emerging trending topics through keyword analysis, and to identify the most productive countries, authors and journals. 9619 articles on meningioma published between 1980 and 2023 were downloaded from the Web of Science (WoS) database and statistically analyzed. In this study, various bibliometric techniques were utilized, including trend keyword analysis, thematic evolution analysis, factor analysis, conceptual structure analysis, citation and co-citation analyses. Bibliometric network visualization maps were created to identify trend topics, citation analysis and cross-country collaborations. The Exponential Smoothing estimator was used to predict article productivity in the coming years. The first 3 countries that contributed the most to the literature were respectively; USA (2664, 27.7%), Japan (972, 10.1%), Germany (943, 9.8%). The first three most productive journals were respectively; Journal of Neurosurgery (number of article = 496), World Neurosurgery (399), Acta Neurochirurgica (378). The most productive author was Mcdermott MW (number of article = 88) and the most active institution was the University of California System (number of article = 470). In addition to high-grade meningiomas, the most studied topics from past to present have been magnetic resonance imaging, recurrence, radiation therapy, and skull base. As a result of the analyses to determine trend topics, the subjects studied in recent years were diagnostic and imaging methods, surgical and treatment methods, prognosis and survival, epidemiology and quality of life, and with the advancement of technology, machine learning and prediction models. Scientific collaboration was seen primarily in articles from western countries, especially the USA, European countries, and Canada. However, there was also a not insignificant effect in developing countries such as China, India, and Turkey.
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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: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.110 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".