Alzheimer Disease and Related Cognitive Impairment in Older Adults: A Narrative Review of Screening, Prevention, and Management for Manual Therapy Providers
Bibliographic record
Abstract
Objective: The aim of this narrative review was to review literature relevant to manual therapists about cognitive impairment, together with screening, potential treatment, and prevention modalities. Methods: A literature search of AMED (Allied and Complementary Medicine Database), CINAHL (Cumulative Index of Nursing and Allied Health Literature), PubMed, and MEDLINE was conducted with the search terms "cognitive decline," "cognitive impairment," "screening," and "prevention." We reviewed current screening practices, including functional exams, imaging, and laboratory testing. We reviewed current potential preventive measures and treatments being implemented in practice. Results: We selected 49 resources for this narrative summary. The Montreal Cognitive Assessment and Mini-Mental State Exam are recommended screening tools. Imaging and laboratory testing are not recommended in screening for cognitive decline. Promotion of healthy, active living through physical and mental activities may assist with prevention of cognitive decline. Conclusion: Cognitive decline affects a large proportion of the US population. Recognizing signs and symptoms of this condition starts with individuals, caretakers, family members, and health care providers. Health care providers should utilize the most appropriate screening tools to assess the presence of cognitive conditions.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".