Neuroimaging in traumatic brain injury: a bibliometric analysis
Bibliographic record
Abstract
OBJECTIVE: Mild traumatic brain injury (mTBI), commonly referred to as concussion, accounts for the vast majority of TBI cases globally, yet remains challenging to define and manage due to its heterogeneous presentation and often subtle clinical and radiographic findings. This bibliometric review aims to characterize the global research landscape on TBI, identifying trends, prolific contributors, and key thematic areas in the literature. METHODS: A bibliometric analysis was conducted using a structured search of the Web of Science Core Collection to identify publications focused on neuroimaging and traumatic brain injury. Data were analyzed using VOSviewer to map co-authorship networks, keyword co-occurrence, and citation patterns over the past decade. RESULTS: The analysis revealed a steady increase in publications related to TBI, with prominent contributions from institutions in the United States, Canada, and Australia. Common research themes included sports-related concussion, military blast injuries, pediatric TBI, neurocognitive outcomes, and return-to-play protocols. Despite the high volume of research, heterogeneity in diagnostic terminology and outcome measures remains prevalent. CONCLUSIONS: This bibliometric review highlights the growing scholarly attention to TBI while emphasizing the need for standardization in diagnostic criteria and outcome assessment. Future research should prioritize consensus-building and longitudinal cohort studies to address persistent gaps in knowledge and improve patient care.
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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 | 0.018 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.253 | 0.307 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".