Bibliographic record
Abstract
The Cognitive section of the Alzheimer's Disease Assessment Scale (ADAS-Cog) remains the most widely used cognitive measure in dementia trials although it does not assess attention, executive function, or agnosia. Designed for use in Alzheimer's disease (AD), it may not be ideal in assessing patients with other diagnoses. The ADAS-Cog differentiates between AD patients, patients with Mild Cognitive Impairment, and normal controls. It has been used in trials of drugs for vascular and mixed dementia and dementia with Lewy bodies. It is not clear that the ADAS-Cog is adequate for assessing cognition in frontotemporal dementia. Well-validated aphasia batteries, such as the Western Aphasia Battery, can be used to assess language. Brief tests of frontal function such as the Frontal Assessment Battery or the Executive Interview might be useful additions in frontotemporal dementia trials. The most widely used assessment tool for patients with advanced dementia is the Severe Impairment Battery. The domains tested are analogous to those assessed by the ADAS-Cog. The Mini-Mental State Exam and the Modified Mini-Mental State Examination are useful in stratifying patients for trial entry. Cognitive measures better tailored to the diseases in question are needed for non-Alzheimer dementias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".