The role of amyloid and tau biomarkers in assessing the effectiveness of drug treatment for Alzheimer’s disease.
Bibliographic record
Abstract
This study aimed to explore the role of amyloid and tau biomark-ers in evaluating the effectiveness of drug therapy for Alzheimer’s disease (AD). A retrospective analysis was performed in 150 AD patients admitted to our hospital from October 2022 to January 2024, and 50 healthy people were selected as the control group. The basic information of patients, including cognitive function and daily living ability, as well as amyloid and tau biomarkers, was compared between the two groups. AD patients were treated with donepezil hydrochloride and memantine tablets, and were divided into valid and invalid groups based on efficacy. Binary logis-tic regression analysis was used to identify factors affecting the effectiveness of AD drug treatment, with the predictive accuracy being assessed using ROC curves.This study revealed that compared with the control group, the MMSE (Mini-Mental State Examination), MoCA (Montreal Cognitive Assessment), and Aβ1-42 in the AD group decreased, while T-tau and P-Tau-181 increased (p<0.05). Drug treatment was con-sidered effective in 107 out of 150 AD patients. Education years, daily exercise, Aβ1-42, T-tau, and P-Tau-181 are all factors that affect the effectiveness of AD drug treatment. The changes in serum levels of Aβ1-42, T-tau, and P-Tau-181 can all be used to evaluate the effectiveness of AD drug treatment, with AUC values of 0.869, 0.815, and 0.800, respectively. The combined evaluation of the three factors has an AUC of 0.977. Drug therapy can improve the clinical efficacy of most AD patients. The years of education, exercise, Aβ1-42, T-tau and P-Tau-181 are the influencing factors of the efficacy of AD drug treatment. The efficacy of AD drug treatment can be evaluated by detecting the changes of serum Aβ1-42, T-tau and P-Tau-181 levels in clinical practice, and the combined evaluation value of the three is higher than the individual values.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".