MicroRNA biomarkers for diagnosis of mild traumatic brain injury and prediction of persistent symptoms: A prospective cohort study
Bibliographic record
Abstract
The diagnosis of mild traumatic brain injury (mTBI) and early identification of patients who have persistent symptoms remains challenging. Symptoms are variably reported, and tests for cognitive impairment require specific expertise. The aim of this study was to assess the ability of plasma micro-ribonucleic acid (miRNA) biomarkers to distinguish between patients with mTBI and healthy controls. A secondary aim was to assess whether miRNA biomarker levels on the day of injury could predict persistent symptoms on day 7. Injured patients presented to an adult, tertiary referral hospital emergency department and were diagnosed with isolated mTBI (n = 75). Venous blood samples were collected within 6 h of injury. Symptom severity was assessed using the Rivermead Post-Concussion Symptom Questionnaire (RPQ) on the day of injury and at 7 days post-injury. The comparator group (n = 44) were healthy controls without any injury, who had bloods sampled and symptom severity assessed at the same time-point. Patients after mTBI reported higher symptom severity and had worse cognitive performance than the control group. Plasma miR423-3p levels were significantly higher among mTBI patients acutely post-injury compared to healthy controls and provided moderate discriminative ability (AUROC 0.67; 95 %CI: 0.57-0.77). None of the assessed miRNA biomarkers predicted persistent symptoms at 7 days. Plasma miR423-3p levels measured within 6 h of injury can discriminate for mTBI compared to healthy controls, with potential utility for screening after head injury or as an adjunct to the diagnosis of mTBI. Acute plasma miRNA levels did not predict patients who reported persistent symptoms at 7 days.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".