miR-195 and miR-497 in acute stroke and their correlations with post-stroke cognitive impairment.
Bibliographic record
Abstract
OBJECTIVE: To quantify the expression of miR-195 and miR-497 in acute stroke and to evaluate their correlations with post-stroke cognitive impairment. METHODS: A total of 108 patients with acute stroke admitted to our hospital from January, 2019 to June, 2020 were enrolled as a patient group, and 76 healthy volunteers were recruited as a normal group. Levels of serum miR-195 and miR-497 in the two groups were quantified. Neurological and cognitive functions were tested by National Institutes of Health Stroke Scale (NIHSS) and Montreal Cognitive Assessment (MoCA), respectively. Diagnostic value of serum miR-195 and miR-497 in acute stroke was evaluated by receiver operating characteristic (ROC) curve, and independent risk factors were determined by multivariate logistic regression. RESULTS: Levels of serum miR-195 and miR-497 increased in acute stroke. The area under the curve (AUC) of serum miR-195 in the diagnosis of acute stroke was 0.901, while that of serum miR-497 was 0.922. Levels of miR-195 and miR-497 were positively correlated with NIHSS score and negatively correlated with MoCA score. Logistic regression analysis demonstrated that family history of stroke, diabetes, hypertension, NIHSS score, MoCA score, miR-195, and miR-497 were independent risk factors for acute stroke. CONCLUSION: Serum miR-195 and miR-497 are elevated in acute stroke and associated with the loss of neurologic and cognitive functions. They may be biomarkers for diagnosis and prognosis of acute stroke.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".