Deep brain stimulation of the nucleus basalis of Meynert in severe Alzheimer’s disease
Bibliographic record
Abstract
Background: Alzheimer's disease (AD) is increasingly prevalent, leading to severe cognitive decline and a diminished quality of life for patients. Nucleus basalis of Meynert deep brain stimulation (NBM-DBS) is a potential treatment approach. Objective: This study aims to assess the efficacy and safety of NBM-DBS for AD patients. Methods: We conducted a clinical study involving 6 patients with severe AD who received NBM-DBS. The treatment's safety and efficacy were evaluated using cognitive function tests (Mini-Mental State Examination, Montreal Cognitive Assessment, Alzheimer's Disease Rating Scale- cognitive subscale, Clinical Dementia Rating) and assessments of neuropsychiatric symptoms and sleep disorders (Functional Activity Questionnaire, Functional Independence Measure, Zarit Burden Interview, Hamilton Anxiety Rating Scale, Hamilton Depression Rating Scale, Neuropsychiatric Inventory, Pittsburgh Sleep Quality Index). Results: NBM-DBS was safe, with no severe adverse events. It improved cognitive functions and self-care abilities without altering the disease's progression. Notably, NBM-DBS significantly alleviated neuropsychiatric symptoms and sleep disturbances. Conclusions: NBM-DBS could be a promising therapeutic approach for severe AD, particularly for managing neuropsychiatric symptoms and sleep disorders. Further research is warranted to confirm these preliminary findings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".