Klinikai neuropszichológiai tesztek magyarországi normatív adatai II.
Bibliographic record
Abstract
INTRODUCTION: One basis of clinical neuropsychology is the application of objective, standardized measurements. Several internationally widespread measurements of memory and learning do not have normative data of the Hungarian population, hence it is crucial to provide a basis for future reference. OBJECTIVE: The purpose of this study was to provide normative data about neuropsychological instruments measuring executive functions, memory and verbal learning skills in relation to demographic factors. METHOD: Rey Auditory Verbal Learning Test (RAVLT), Montreal Cognitive Assessment (MoCA) and Prospective and Retrospective Memory Questionnaire (PRMQ) were administered to an adult, Hungarian representative sample (age, sex, education). RESULTS: Higher educated participants performed better on PRMQ, MoCA and RAVLT. Participants with primary education were identified as a risk group for poor verbal learning skills, executive functions and they committed more memory errors. Age had no significant effect on the results of PRMQ, while on MoCA and RAVLT a significant decline in performance was observed with the passage of lifetime. Females performed better in immediate and delayed recall on RAVLT. CONCLUSION: Application of the presented neuropsychological tests is recommended in clinical practice and scientific research as well. The presented normative data could be a valuable reference point for future studies and practical application, furthermore a basis for early identification of neurocognitive deficits. Orv Hetil. 2023; 164(16) 618-629.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".