[PP.29.23] COGNITIVE DYSFUNCTION IN HYPERTENSIVE PATIENTS FROM ROMANIA - WHICH TEST TO USE IN CLINICAL PRACTICE?
Bibliographic record
Abstract
Objective: Screening for cognitive dysfunction in clinical practice may be challenging, because there are several tests available for testing different aspects of human cognition. The aim of our study was to compare three cognitive impairment screening tests for detection of cognitive impairment in consecutive hypertensive patients from a cardiovascular rehabilitation unit from Romania. Design and method: We tested the clinical usefulness of three cognitive impairment tests: the MMSE, the Montreal Cognitive Asessment test (MOCA), and the General Practitioner Assessment of Cognition (GPCOG) test. Depression as a confounding factor was tested with the 13 item short form of the Beck depression inventory (BDI) test. 35 consecutive hypertensive patients were included (average age 67.1, standard deviation 10.6 years). We observed the frequency of cognitive dysfunction detected according to the different tools, and observed the clinical applicability of different tests. MRI was perfomed in 5 patients. Results: Cognitive impairment test were positive in the following percent of patients: MOCA test 71.4% (25 out of 35), GPCOG 34.2% (12 out of 35), MMSE 25.7% (9 out of 35). Depression was present among the studied patients: mild 9 (25.7%), moderate 5 (14.2%), severe 4 (11.4%). All patients with a positive MMSE were also MOCA positive, and all of them needed informant interview according to GPCOG questionnaire. In two cases the informant interview showed no cognitive impairment, but later was clarified that the informant spouse also suffered from cognitive impairment certified by MMSE. Patients with major depression according to BDI were all suffering from cognitive impairment according to MOCA, one out of four according to MMSE, and two out of four according to GPCOG. Brain MRI was performed in 5 patients, all showing microvascular cerebral lesions, also in patients with normal cognitive function according to MMSE test. Conclusions: There are important differences among cognitive impairment tests in the clinical practice. MOCA test is the most sensitive, MMSE is the least sensitive one. GPCOG has the strength and inconvenience of the informant interview. MRI lesions can be detected even in patients with a negative MMSE test.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.020 |
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".