Bibliographic record
Abstract
The aim of the thesis is to analyse the psychometric properties of the MINT (The Multilingual Naming Test), which is part of the UDS 3 neuropsychological battery. The content of the theoretical part is a description of the MINT test, which is based on the principle of visual naming of objects. Cognitive functions are mentioned, especially fatal functions including their impairments. In the empirical part, the methodology is presented, including the characteristics of the research population (consisting of a group of healthy persons, individuals with subjective cognitive decline and with mild cognitive impairment), measurement tools, statistical analysis, etc., and the results of the statistical analysis performed, which are further discussed. The results demonstrated the ability of the MINT to discriminate between healthy individuals and individuals with mild cognitive impairment, but not the ability to discriminate between individuals with subjective cognitive deficits. The study also shows an acceptable level of reliability of the MINT and its significant correlation with other tests of fatal function and the Montreal Cognitive Assessment (MoCA). The work shows that the MINT test can be a useful tool for diagnosing naming disorders in patients with mild cognitive impairment. Key words: MINT 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 | 0.014 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".