[Validation of the Hungarian version of Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) in patients with mild cognitive impairment].
Bibliographic record
Abstract
INTRODUCTION: Early recognition of mild cognitive impairment (MCI) has increasing clinical relevance in the treatment process of dementia, since it is considered as prodromal period. A great variety of instruments have been developed for measuring cognitive performance of the demented patients. The cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-Cog) is one of the most frequently applied instrument to determine the severity of dementia and the efficiency of pharmacotherapy. The aim of this study is to examine the sensitivity parameters of the Hungarian ADAS-Cog in differentiating healthy elderly from MCI patients, furthermore to compare the sociodemographic data of the two groups. METHODS: Fourty-five patients with MCI and 47 healthy subjects (HS) participated in the study. Their age variated between 52 and 88 years, the mean age was 68.8 (standard deviation=8.6). The mean of the years of education was 11.8 (standard deviation=3.5). Mental state was determined by ADAS-Cog and Mini-Mental State Examination (MMSE) and Beck Depression Inventory (BDI) was used to exclude depression. Data analysis was performed with SPSS 17. RESULTS: There were no significant differences between the two groups considering the sociodemographic data. The total score of ADAS-Cog is the most sensitive index (AUC: 0.875, sensitivity: 95.6%) for determining MCI, although the ratio of false positive cases was very high (specificity: 70.2%). The cut-off scores of the ADAS-Cog in the Hungarian sample were higher than the findings in previous researches. Positive correlation between age and ADAS-Cog total score was only significant in the HS group. On the other hand, negative correlation was found between education and ADAS-Cog total score in the MCI group. CONCLUSION: These results indicate that the currently used Hungarian ADAS-Cog is able to distinguish between MCI patients and HS groups. However, the adaptation of the Hungarian version will be necessary during the further standardization process including the cultural and linguistic aspects.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".