The role of biomarkers of endothelial dysfunction in predicting the progression of mild cognitive impairment in patients with cardiovascular risk factors
Bibliographic record
Abstract
OBJECTIVE: To develop a predictive model to assess the risk of progression of mild cognitive impairment (MCI) within 12 weeks in patients with cardiovascular risk factors using biomarkers of endothelial dysfunction. MATERIAL AND METHODS: The study included 287 patients (mean age 64.3 years, 123 (42.9%) men) who met the inclusion criteria. All participants, at baseline and after 12 weeks, underwent neuropsychological testing using the Montreal Cognitive Assessment (MoCA) and laboratory blood tests to determine the levels of markers of endothelial inflammation (C-reactive protein (CRP), monocyte chemoattractant protein) and endothelial dysfunction (endothelin-1, endothelial NO-synthase (eNOS), endothelial growth factor, desquamated endothelial cells, S100B, von Willebrand factor, fibrinogen). During the study, patients took stable basic therapy. The demographic and anamnestic data, the results of neuropsychological testing and laboratory tests were used to construct a model of predictors that determine the trajectory of MCI using binary logistic regression, followed by calculation of its threshold indicator as a value for predicting the progression of MCI. RESULTS: =0.0042) are independent predictors for the MCI progression. ROC-analysis showed a high predictive ability of the model with a threshold value of 0.4 (sensitivity 82.1%, specificity 72.3%). CONCLUSION: Age, a history of IS, disorders of executive functions and speech, together with elevated values of CRP, fibrinogen, eNOS are important conditions for predicting the progression of MCI in patients with cardiovascular risk factors. These predictors and the risk of MCI progression calculated on their basis can be used as a tool for early diagnosis of dementia and in developing measures to prevent the progression of non-dementia cognitive impairment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".