P-448 - The validation of ADAS-Cog: cognitive performance and characteristics of patients with alzheimer’s disease or depressive pseudodementia
Bibliographic record
Abstract
The cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-Cog) has been established internationally as an instrument for the assessment of treatment efficacy and cognitive performance in clinical trials. There is no data about the psychometric characteristics of ADAS-Cog in Hungarian sample. This study is a part of the Hungarian standardization process of ADAS-Cog. It is crucial to examine the cognitive performance of patients with pseudodementia caused by depression (D) because of its’ similarities with Alzheimer's disease (AD). The objective of the study was to analyze the characteristics of the cognitive subscale for further validation purposes. The study aimed at analyzing the ADAS-Cog performance of patients with D and AD in a Hungarian sample to make future studies more accurate through more exact differentiation between the two diseases. Forty-seven normal elderly control (CG) subjects, 66 AD patients and 39 patients with D participated in the study. The mental state and the severity of depressive symptoms of the participants were assessed by the means of ADAS-Cog, Mini Mental State Examination (MMSE) and Beck Depression Inventory. While the performance of the two patient groups differed from the CG, the two groups are overlapping and the characteristic of the ROC curve indicates that the differentiation is mediocre (AUC = 0.8 Sensitivity = 62.1%, Specificity = 89.7%). The results suggest that pseudodementia should be considered during the design of studies using ADAS-Cog. As the cognitive subscale is not suitable to differentiate between AD and pseudodementia additional measures like BDI should be administered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".