Theracurmin Supplementation May be a Therapeutic Option for Older Patientswith Alzheimer’s Disease: A 6-Month Retrospective Follow-UpStudy
Bibliographic record
Abstract
BACKGROUND: Alzheimer's Disease (AD) is still a great global challenge and agents with various mechanisms represent a promising therapeutic opportunity. Theracurmin, a very highly absorbable curcumin formulation, was shown to improve memory and attention in non-demented people. OBJECTIVE: The aim of the study was to investigate the effect of Theracurmin on disease course in elderly patients with mild cognitive impairment (MCI) and AD. METHODS: This follow-up study was performed retrospectively on 93 patients with MCI or AD. All patients underwent comprehensive geriatric assessment, including Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), clock-drawing test, activities of daily living (ADL), at baseline and at the end of the 6th month. 19 patients with AD and 17 with MCI were treated with Theracurmin 180 mg/day per oral. RESULTS: MMSE, MOCA and instrumental ADL scores decreased in AD patients not treated with Theracurmin (p<0.001, p=0.011, and p=0.004, respectively), whereas these scores remained stable in those treated with Theracurmin. This stabilization in the instrumental ADL was also observed in MCI patients treated with Theracurmin. During the follow-up, three MCI patients who did not receive Theracurmin progressed to AD, whereas only one patient progressed in those who received it. CONCLUSION: Theracurmin seems to be a therapeutic option for elderly patients with AD and MCI via providing stabilization of the disease course by preventing progressive loss in cognitive functions and ADLs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".