Effects of whole-head 810 nm near-infrared therapy on cognitive and neuropsychiatric symptoms in Alzheimer's disease: A pilot study
Bibliographic record
Abstract
Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by significant cognitive and behavioral impairments. Near-infrared (NIR) light treatment has shown potential in cognitive improvement in previous studies. However, clinical trials of NIR for AD remain limited. Objective This study investigated the safety and effects of whole-head 810 nm NIR therapy in AD patients, including long-term efficacy. Methods An open-label pilot study on whole-head NIR treatment for AD patients was conducted. Nine AD patients completed 4-month treatment (810 nm, 100 mW/cm², 30 min/session, 6 sessions weekly). Safety and efficacy were evaluated at baseline, months 2 and 4, and 2-month post-treatment. Results After four months of whole-head NIR treatment, mean changes from baseline on the Mini-Mental State Examination were 3.2 ( p = 0.02). Mean changes from baseline on the Alzheimer's Disease Assessment Scale-Cognitive were −5.0 ( p = 0.05), mean changes from baseline on the Montreal Cognitive Assessment were 1.9 ( p = 0.12). Mean changes from baseline on the Neuropsychiatric Inventory were −4.2 ( p = 0.47). These benefits were sustained two months at least. With no device-related adverse effects were reported. Conclusions Whole-head 810 nm NIR light is safe and offers promising benefits for AD patients. To fully confirm its efficacy, durability, and underlying mechanisms, further large-scale randomized controlled trials are necessary.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".