Validity of Serum miRNA 93 and miRNA 191 to Reduce Unnecessary Computed Tomography in Patients With Mild Head Trauma
Bibliographic record
Abstract
BACKGROUND: Indication for the appropriate use of cranial computed tomography (CCT) in patients with mild head trauma (MHT) based on history and physical examination alone remains unclear. Recent studies have been reported that 90% of patients with MHT who undergo CCT under the present clinical decision rules have no clinically important brain injuries. We aimed to investigate whether peripheral blood expression of microRNA 93 (miR93) and microRNA 191 (miR191) in patients with MHT can predict the presence or absence of intracranial injury, reducing the unnecessary use of CCT. METHODS: Fifty-nine consecutive adult patients with isolated MHT undergoing CCT based on the clinical decision guidelines of the New Orleans criteria and 91 age- and sex-matched controls were enrolled in this prospective observational cohort study. Patients were divided into two groups: those without or with traumatic intracerebral or extracerebral lesions identified by CCT. Patients were further divided into two subgroups based on the presence or absence of traumatic parenchymal lesions defined as traumatic brain injury (TBI). RESULTS: Mean serum miR93 and miR191 levels differed significantly between study groups. Of the 79 patients investigated, 16 exhibited trauma-relevant lesions on CCT scan (CCT+). With a cut-off limit of 0.15, miR191 had an area under the curve value of 0.765 (0.640 - 0.889), with sensitivity of 68.1% and specificity of 68.8% in CCT+ patients. Compared to MHT patients without TBI, mean serum miR191 levels were markedly elevated in patients with TBI. However, miR93 levels did not exhibit significant changes in either group. CONCLUSIONS: Circulating miRNA levels increased after MHT and differentiated patients with and without intracranial or extracranial lesions demonstrable on CCT. Adding the measurement of serum miRNAs particularly miR191 to the clinical decision rules for a CCT scan in patients with MHI could allow a reduction in scans.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".