P1‐227: Comparative and combined accuracies of objective versus subjective screening instruments in distinguishing Alzheimer's disease, mild cognitive impairment, and age‐related cognitive decline
Bibliographic record
Abstract
The aim of the study was to compare the utility and diagnostic accuracy of several widely used objective and subjective measures utilized in the diagnosis of Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) and Age Related Cognitive Decline (ARCD) in a clinical cohort as well as evaluate the potential improvement in diagnostic and classification accuracy when cognitive screening instruments are used in combination with subjective measures of functional impairment. One hundred and five AD, 66 MCI and 43 ARCD were evaluated using a standard clinical evaluation that included the Mini Mental Status Exam (MMSE), Montreal Cognitive Assessment (MOCA) and Alzheimer's Disease Assessment Scale - Cognitive Subscale (ADAS-Cog). Informant based measures of functional impairment including the Alzheimer's disease Caregivers Questionnaire (ADCQ) and Activity of Daily Living Scale (ADL's) were also completed by caregivers. Diagnostic accuracy and optimal cut-off scores were calculated for each individual measure and subjective assessments were combined with cognitive screening measures to evaluate the potential improvement in overall diagnostic accuracy. The MMSE, MOCA and ADAS-Cog all offer good diagnostic and classification accuracy for differentiating between AD vs. ARCD and AD vs. MCI. The MOCA and ADAS-cog were superior to the MMSE at differentiating between patients diagnosed with MCI vs. ARCD. Measures of functional impairment also provided good diagnostic and classification accuracy for differentiating between AD and other diagnosis, but were less accurate at differentiating between MCI vs. ARCD. Combing a subjective measure of functional impairment with the MOCA significantly improved diagnostic accuracy between patients with AD vs. MCI. The findings support data, which indicates that the MOCA is superior to the MMSE as screening tool, particularly in discerning the earliest symptoms of cognitive decline. Similarly, the ADAS-cog also demonstrated good diagnostic and classification accuracy in differentiating between diagnoses. Results also indicated that the addition of a subjective measure of functional impairment can improve overall diagnostic accuracy. ROC curves for each pairwise comparison of AD, MCI and AAMI and each of the diagnostic tests considered. AUC scores and cut-off thresholds for each test are shown Table 2.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".