Cognition, Gender, and Functional Abilities in Alzheimer's Disease: How are They Related?
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated in detail which factors influence activities of daily (ADL) in Alzheimer's disease (AD). OBJECTIVE: To assess the influence of cognitive, gender, and other factors on ADL in patients with mild to moderate AD. METHODS: This study is part of the Prospective Registry on Dementia in Austria (PRODEM) project, a multicenter dementia research project. A cohort of 221 AD patients (130 females; means: age 76 years, disease duration 34.4 months, MMSE 22.3) was included in a cross-sectional analysis. Everyday abilities were assessed with the Disability Assessment for Dementia scale, and cognitive functions with the CERAD plus neuropsychological test battery. Two models of multiple linear regressions were performed to find factors predicting functional decline, one entering demographical and disease related factors, and a joint model combining demographical and disease variables with neuropsychological scores. RESULTS: Non-cognitive factors explained 18%, whereas the adding of neuropsychological variables explained 39% of variance. Poor figural and verbal memory, constructional abilities, old age, longer disease duration, depression, and male gender were independent risk factors for reduced ADL. Instrumental and basic ADL were predicted by similar factors, except gender (predicting only instrumental ADL) and phonological fluency (predictor of basic ADL). CONCLUSION: In addition to demographical factors, disease duration, and depression, neuropsychological variables are valuable predictors of the functional status in AD in an early disease stage.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".