Serum miRNA Detection-based Alzheimer’s disease prediction regression model
Bibliographic record
Abstract
With the deepening of Alzheimer’s disease (AD) research, serum miRNA has attracted widespread attention as a potential biomarker. Traditional diagnostic methods for AD have certain limitations, such as reliance on clinical symptoms and neuroimaging examinations, which lack sensitivity (Sen) and specificity (Spe) for early diagnosis. Therefore, this article aimed to explore the expression levels of serum miRNA in AD patients and its clinical significance, to construct an AD prediction regression model based on serum miRNA detection. This article found no statistical differences in gender, underlying diseases, age, triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) between the control group (healthy individuals) and the AD group, but obvious distinctions were observed in Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Alzheimer’s Disease Assessment Scale-cognitive part (ADAS-cog), and Activities of Daily Living (ADL) scores. Further analysis revealed obvious distinctions in miR-31, miR-93, miR-124-3p, miR-143, miR-146a, and miR-218-5p between the two groups, with miR-124-3p showing the best diagnostic effect, followed by miR-218-5p. Based on these findings, this article constructed an AD prediction regression model, and the experimental results indicated that the model has high Sen, Spe, and accuracy (Acc) in the early diagnosis of AD, reducing the error rate of subsequent diagnoses and providing new ideas and methods for the early diagnosis of AD.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".