Systematic Review of Clinical Studies Examining Biomarkers of Brain Injury in Athletes after Sports-Related Concussion
Bibliographic record
Abstract
The aim of this study was to systematically review clinical studies examining biofluid biomarkers of brain injury for concussion in athletes. Data sources included PubMed, MEDLINE, and the Cochrane Database from 1966 to October 2013. Studies were included if they recruited athletes participating in organized sports who experienced concussion or head injury during a sports-related activity and had brain injury biomarkers measured. Acceptable research designs included experimental, observational, and case-control studies. Review articles, opinion papers, and editorials were excluded. After title and abstract screening of potential articles, full texts were independently reviewed to identify articles that met inclusion criteria. A composite evidentiary table was then constructed and documented the study title, design, population, methods, sample size, outcome measures, and results. The search identified 52 publications, of which 13 were selected and critically reviewed. All of the included studies were prospective and were published either in or after the year 2000. Sports included boxing (six studies), soccer (five studies), running/jogging (two studies), hockey (one study), basketball (one study), cycling (one study), and swimming (one study). The majority of studies (92%) had fewer than 100 patients. Three studies (23%) evaluated biomarkers in cerebrospinal fluid (CSF), one in both serum and CSF, and 10 (77%) in serum exclusively. There were 11 different biomarkers assessed, including S100β, glial fibrillary acidic protein, neuron-specific enolase, tau, neurofilament light protein, amyloid beta, brain-derived neurotrophic factor, creatine kinase and heart-type fatty acid binding protein, prolactin, cortisol, and albumin. A handful of biomarkers showed a correlation with number of hits to the head (soccer), acceleration/deceleration forces (jumps, collisions, and falls), postconcussive symptoms, trauma to the body versus the head, and dynamics of different sports. Although there are no validated biomarkers for concussion as yet, there is potential for biomarkers to provide diagnostic, prognostic, and monitoring information postinjury. They could also be combined with neuroimaging to assess injury evolution and recovery.
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".