A-15 Network Analysis Of Sport-Related Concussion Research During The Past Decade (2010–2019)
Bibliographic record
Abstract
Abstract Objective The purpose of this study was to apply network analyses to evaluate trends in the literature using a comprehensive search of original, peer-reviewed research articles involving human participants with sport-related concussion (SRC) published between January 1, 2010 and December 31, 2019. Data Selection Articles were identified in a comprehensive online search using key terms to encompass all forms of SRC or mTBI and entered into a clustering algorithm (vosViewer). Each cluster (e.g., journal, institution, author, keyword) is named for the hub, or most highly interconnected individual node. Data Synthesis The online search yielded 6,130 articles, 528 journals, 7,598 authors, 1,966 institutions, and 3,293 keywords. The analysis supported five 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), 5. Neuropsychology (n = 7). The analysis identified four institutional clusters: 1. University of North Carolina (n = 19), 2. University of Toronto (n = 19), 3. University of Michigan (n = 11), 4. University of Pittsburgh (n = 10). Five primary author clusters were identified: 1. A. Kontos (n = 32), 2. G. Iverson (n = 27), 3. M. McCrea (n = 27), 4. S. Broglio (n = 25), 5. Z. Kerr (n = 16). In regards to keywords, central topics included: 1. Epidemiology (n = 14), 2. Rehabilitation (n = 12), 3. Biomechanics (n = 11), 4. Imaging (n = 10), 5. Assessment (n = 9). 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) focused on new topic areas including treatment/rehabilitation.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.043 | 0.048 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".