Recent Advances in Screening for Mild Cognitive Impairment and Alzheimer Disease
Bibliographic record
Abstract
In recent years, scientific understanding of the pathophysiology underlying Alzheimer disease (AD) has advanced substantially. Among the most transformative of discoveries is the existence of biomarkers, such as Aβ42, which can manifest in the central nervous system decades before the onset of disease-associated dementia. By detecting these biological entities early, clinicians can close diagnostic delays and substantially improve outcomes for patients with AD. With prompt news of a diagnosis, patients can initiate long-term planning and devise goals for treatment while their cognition is relatively intact. To differentiate among different forms of dementia, neurologists and supporting clinicians should additionally capitalize on the availability of validated screening tools. Increasingly adopted, tests such as the Montreal Cognitive Assessment yield highly sensitive, specific findings that can improve the standard of care. These results, when paired with insights gleaned from patient histories and clinical examinations, can further inform treatment-decision making and help ensure that patients receive care tailored to their unique circumstances and needs.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".