COGNITIVE FUNCTION, CAROTID STRUCTURAL CHANGES AND KIDNEY FUNCTION: ARE THERE RELATIONSHIP IN PATIENTS WITH RESISTANCE HYPERTENSION?
Bibliographic record
Abstract
Objective: To evaluate the relationship between cognitive function, carotid structural changes and kidney function in patients with resistance hypertension (RH). Design and method: In 98 pts with RH (average age 54,3±2,5; 55 males) we measure ultrasonographically carotid diameter (CD) and intima-media thickness (IMT). Cognitive function was assessed in all pts by Montreal cognitive assessment (MoCA) test. All patients underwent full clinical and laboratory screening evaluation. Kidney function was estimated by glomerular filtration rate (GFR) using the CKD-EPI creatinine equation. All pts were divided into 2 groups depending on the value of GFR: group I (n=59) - GFR > 60 (ml/min/1,73m2) and group II (n=39) – GFR < 60 (ml/min/1,73m2). Results: Patients with GFR < 60 (ml/min/1,73m2) had significantly greater carotid structural changes: pts of gr.I had CD 7,79±0,07 mm and IMT 1,22±0,02 mm vs pts of gr.II 8,92±0,08 mm (p<0,05) and 1,77±0,03 mm (p<0,05) accordingly. Cognitive function was more impaired in pts gr II: MoCA score of gr I pts was 25,3±1,8 and gr II pts – 22,1±1,4. GFR was significantly correlated with MoCA score and carotid structure (MoCA score: r=0,61, p<0,05, CD: r=0,55, p<0,05, IMT: r=0,68, p<0,05). Adjustment for age, blood pressure, BMI and gender did not abolish association between GFR and MoCA score, parameters of carotid structure. Conclusions: There is relationship between kidney function, carotid structure and cognitive function in pts with resistance hypertension. GFR may have prognostic value for cognitive function damage in patients with resistance hypertension.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".