Comparison of currently used cognitive screening tests in different types of neurodegenerative diseases
Bibliographic record
Abstract
This thesis aims to compare the commonly used cognitive screening tests such as Mini- Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Picture Naming and Immediate Recall Test (PICNIC) in a clinical population. Previous studies often focused on specific populations (e.g., patients with Alzheimer's disease), but this sample includes a wide spectrum of neurodegenerative diseases as well as individuals without noticeable cognitive deficit. Therefore, we were interested in examining the performance of these screening tests in this heterogeneous population. The theoretical part extensively describes mild cognitive impairment, various neurodegenerative diseases, and the employed tests. In the empirical part, the collected data from patients (N = 35) at a coeducational geropsychiatric department of the Psychiatric Hospital in Dobřany are presented. These data were subjected to statistical analyses to assess the normality of the sample distribution, relationships with demographic indicators, and correlations between the tests. Even in the utilized specific diverse sample, statistically significant correlations were found between the individual tests. Key words: neurodegeneration, screening tests, Mini-Mental State Examination, Montreal Cognitive Assessment, Picture Naming and...
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".