The Efficacy of miRNAs to Diagnose and Monitor Persistent Post- Concussive Syndrome: A Scoping Review
Bibliographic record
Abstract
Persistent post-concussive symptoms (PPCS) refer to concussion symptoms that persist beyond 14 days (Johnson et al., 2018). Diagnosing PPCS relies heavily on subjective patient reporting and clinical examination, due to a lack of diagnostic tools. Biomarkers offer a solution to the current problem; specifically, micro ribonucleic acids (miRNAs) are a potential family of biomarkers that can be used as an objective standard for diagnosing and managing PPCS. Moreover, this type of biomarker can be collected through saliva samples thus offering a minimally invasive collection method. The purpose of this scoping review is to determine the efficacy of miRNAs as an objective biomarker that can confirm PPCS and track recovery. This review used the Arksey and O’Malley framework, which include PubMed, Physical Education Index, SPORTDiscus, and Web of Science databases. The peer-reviewed primary articles were published in English between 2018 and June 2024. The initial search yielded 913 articles, and only 4 articles reached the final review stage. Together, 27 unique miRNAs were identified throughout the four papers, many of which propose a strong link with particular PPCS symptoms. The findings of this scoping review were consistent with previous primary and secondary research, which support the clinical use of miRNAs for PPCS diagnosis and monitoring. Further longitudinal research with a more diverse patient population, and investigations on the link between symptoms and miRNA expression will help to better define the utility of miRNA in the context of PPCS.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".