Bibliometric Analysis of Chronic Traumatic Encephalopathy Research from 1999 to 2019
Bibliographic record
Abstract
Research on chronic traumatic encephalopathy (CTE) has increased over the past two decades. However, few studies have statistically analyzed these publications. In this work, we conducted a bibliometric analysis of studies on CTE to track research trends and highlight current research hotspots. Relevant original articles were obtained from the Web of Science Core Collection database between 1999 and 2019. CiteSpace and VOSviewer software were used to perform analysis and visualization of scientific productivity and emerging trends. Our results show that the publications related to CTE dramatically increased from four publications in 1999 to 160 publications in 2019. The United States dominated this field with 732 publications (75.934%), followed by Canada with 88 publications (9.129%). Most of related publications were published in the journals with a focus on molecular biology, immunology, neurology, sports and ophthalmology, as represented by the dual-map overlay. A total of 11 major clusters were explored based on the reference co-citation analysis. In addition, three predominant research topics were summarized by clustering high-frequency keywords: epidemiological, clinical and pathological studies. The research frontiers were the diagnosis of diseases using new neuroimaging techniques, and the investigation of the molecular mechanism of tau aggregation. This study provides researchers with valuable guidance in the selection of research topics.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.167 | 0.236 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".