MicroRNA Biomarkers on Day of Injury Among Patients with Post Concussive Symptoms at 28-Days: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: After mild traumatic brain injury (mTBI), some patients experience symptoms that persist for weeks to months. Recovery from mTBI is primarily assessed using selfreported symptom questionnaires. Blood biomarkers, including microRNA species, have shown promise to assist diagnosis of mTBI, however, little is known about how blood microRNA measures might predict symptom recovery. OBJECTIVE: The aim of this study was to investigate the variances in plasma microRNAs on the day of injury between individuals with mTBI who report post-concussive symptoms at the 28- day mark and those who do not. METHODS: Patients who presented to an adult, tertiary referral hospital emergency department on the day of the injury and were diagnosed with isolated mTBI (n=35) were followed up for 28 days. Venous blood samples were collected and symptom severity was assessed using the Rivermead Post-Concussion Symptom Questionnaire (RPQ) on the day of injury and at 28 days. Patients who reported ongoing symptoms of total RPQ score ≥10 or at least one symptom severity ≥2, were compared to those with lesser symptom severity or symptom resolution. RESULTS: There were 9 (25.7%; 95%CI: 12.5-43.3) patients who reported persistent symptoms. Day of injury plasma miR-223-3p levels were significantly higher in individuals with ongoing symptoms compared to those without, however, no such differences were observed for miRs 142- 3p, 423-3p, 32-5p, 144-3p, and let-7f-5p. CONCLUSION: Acute plasma miR-223-3p levels appear to detect patients who later have persistent symptoms after mTBI. The results demonstrate the potential utility for such biomarkers to assist in decisions towards early referral for therapy after mTBI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".