Network Analysis of Sport-Related Concussion Research During the Past Decade (2010–2019)
Bibliographic record
Abstract
CONTEXT: Research into sport-related concussion (SRC) has grown substantially over the past decade, yet no authors to date have synthesized developments over this critical time period. OBJECTIVE: To apply a network-analysis approach in evaluating trends in the SRC literature using a comprehensive search of original, peer-reviewed research articles involving human participants published between January 1, 2010, and December 15, 2019. DESIGN: Narrative review. MAIN OUTCOME MEASURE(S): Bibliometric maps were derived from a comprehensive search of all published, peer-reviewed SRC articles in the Web of Science database. A clustering algorithm was used to evaluate associations among journals, organizations or institutions, authors, and key words. The online search yielded 6130 articles, 528 journals, 7598 authors, 1966 organizations, and 3293 key words. RESULTS: The analysis supported 5 thematic clusters of journals: (1) biomechanics/sports medicine (n = 15), (2) pediatrics/rehabilitation (n = 15), (3) neurotrauma/neurology/neurosurgery (n = 11), (4) general sports medicine (n = 11), and (5) neuropsychology (n = 7). The analysis identified 4 organizational clusters of hub institutions: (1) University of North Carolina (n = 19), (2) University of Toronto (n = 19), (3) University of Michigan (n = 11), and (4) University of Pittsburgh (n = 10). Network analysis revealed 8 clusters for SRC key words, each with a central topic area: (1) epidemiology (n = 14), (2) rehabilitation (n = 12), (3) biomechanics (n = 11), (4) imaging (n = 10), (5) assessment (n = 9), (6) mental health/chronic traumatic encephalopathy (n = 9), (7) neurocognition (n = 8), and (8) symptoms/impairments (n = 5). CONCLUSIONS: The findings suggest that during the past decade SRC research has (1) been published primarily in sports medicine, pediatric, and neuro-focused journals, (2) involved a select group of researchers from several key institutions, and (3) concentrated on new topical areas, including treatment or rehabilitation and mental health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".