Bibliometric analysis of traumatic brain injury in the neurosciences field from the past 10 years
Bibliographic record
Abstract
Abstract Traumatic brain injury (TBI) is a common neurological disorder that causes severe problems in lack of effective treatment. This study aims to investigate the burgeoning trends of TBI in the field of neurosciences and offer insights for future research. Web of Science (WOS) was used to download data of TBI. The topic of “traumatic brain injury” in the neurosciences research area has been investigated during the period 2015-2024 and analyzed the research trends through VOSviewer, Pajek, and Excel software. According to the search strategy, 2476 articles in journals with an impact factor of 5 or higher were exported. The United States of America (USA) is the most productive, followed by China, Canada, England, and Australia. The top five organizations in terms of publication count include the University of Pennsylvania, the University of Pittsburgh, Harvard Medical School, Uniformed Services University of the Health Sciences, and the University of Melbourne. The journal, Neural Regeneration Research, is the most productive. Shultz, Sandy R. publishes the highest number of articles. Keyword cluster analysis shows that currently researchers' studies mainly focus on TBI, followed by inflammation, neuroinflammation, Alzheimers-disease, and neuroprotection. Conclusively, this review offers a comprehensive summary and analysis of TBI in the neurosciences area. In the last 10 years, the number of high–quality papers in this field has increased significantly, and increasing treatments for TBI have been provided.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.108 | 0.167 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".