Focusing on Earlier Management of Alzheimer Disease
Bibliographic record
Abstract
BACKGROUND: Despite the number of people living with Alzheimer disease (AD), awareness of the early stages of this condition, including mild cognitive impairment due to AD-which poses management challenges-continues to be low. To identify areas for improvement in early AD management, dementia specialists convened in a virtual roundtable meeting. METHODOLOGY: A modified version of the nominal group technique was followed to prioritize specific topics and allow experts to provide their opinions. The overarching topics prioritized and discussed were (1) education and support for primary care physicians on cognitive assessment, detection of mild cognitive impairment, and patient monitoring; (2) nonpharmacological interventions; (3) and the introduction of disease-modifying therapies. CONCLUSIONS: Consensus was achieved regarding the need for educating primary care physicians on identifying people with cognitive impairment and for better diagnostic tools for its detection and early management. Management of mild cognitive impairment due to AD should encompass an adequate follow-up schedule aiming to maintain function for as long as possible, and primary care physicians and patients should be aware of the benefits of nonpharmacological interventions.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".