Role of advanced neuroimaging, fluid biomarkers and genetic testing in the assessment of sport-related concussion: a systematic review.
Bibliographic record
Abstract
Objective To conduct a systematic review of published literature on advanced neuroimaging, fluid biomarkers and genetic testing in the assessment of sport-related concussion (SRC). Data sources Computerised searches of Medline, PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Scopus and Cochrane Library from 1 January 2000 to 31 December 2016 were done. There were 3222 articles identified. Study selection In addition to medical subject heading terms, a study was included if (1) published in English, (2) represented original research, (3) involved human research, (4) pertained to SRC and (5) involved data from neuroimaging, fluid biomarkers or genetic testing collected within 6 months of injury. Ninety-eight studies qualified for review (76 neuroimaging, 16 biomarkers and 6 genetic testing). Data extraction Separate reviews were conducted for neuroimaging, biomarkers and genetic testing. A standardised data extraction tool was used to document study design, population, tests employed and key findings. Reviewers used a modified quality assessment of studies of diagnostic accuracy studies (QUADAS-2) tool to rate the risk of bias, and a modified Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to rate the overall level of evidence for each search. Data synthesis Results from the three respective reviews are compiled in separate tables and an interpretive summary of the findings is provided. Conclusions Advanced neuroimaging, fluid biomarkers and genetic testing are important research tools, but require further validation to determine their ultimate clinical utility in the evaluation of SRC. Future research efforts should address current gaps that limit clinical translation. Ultimately, research on neurobiological and genetic aspects of SRC is predicted to have major translational significance to evidence-based approaches to clinical management of SRC, much like applied clinical research has had over the past 20 years.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".