ADAS-Cog Trajectories Differ from Expected Decline in Dementia Following Repeated Non-Invasive Interventions over 3 Years
Bibliographic record
Abstract
Background and Objectives: Non-pharmaceutical interventions such as cognitive training, transcranial electrical stimulation (tES), and repetitive transcranial magnetic stimulation (rTMS) have shown promise in improving cognitive outcomes in Alzheimer’s disease (AD) and dementia. However, the long-term effects of repeated non-invasive interventions remain unknown. This study investigated whether repeated non-invasive interventions administered over a span of 1 to 3 years were associated with slower cognitive decline compared to typical AD progression, and whether longer no-treatment intervals between treatments predicted greater post-treatment decline. Materials and Methods: Seventy-three participants living with dementia or AD received 2 to 9 blocks of non-invasive treatments (including tES, rTMS, cognitive training). Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) scores were collected longitudinally up to 3 years (36 months), across multiple intervention and assessment sessions. A mixed-effects model was used to estimate the rate of cognitive decline, adjusting for baseline age, sex, and baseline cognition (MoCA) with participants being the random effect. The observed rate of change was compared to a meta-analysis estimate of AD progression. Additionally, a linear mixed-effects model using robust sandwich estimation of standard errors was employed to assess whether the no-treatment interval was associated with changes in ADAS-Cog scores. Results: Participants showed a significantly slower rate of cognitive decline than expected from the AD reference rate (p < 0.001), with many demonstrating stabilized ADAS-Cog scores during their respective treatment periods, ranging from 1 to 3 years. Medication analyses revealed no significant effect of AD medications, antidepressants, antihypertensives, or cholesterol-lowering agents on cognitive outcomes. Furthermore, longer no-treatment intervals were significantly associated with greater post-treatment decline (p < 0.001). Conclusions: Repeated non-invasive treatments seem to slow the rate of cognitive decline in individuals living with dementia when administered over a prolonged period. This study provides evidence supporting the feasibility and effects of personalized long-term non-invasive treatment strategies for dementia.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".