Does the Administration of Curcumin, Compared to Placebo, Change Cognitive Function in Adults Older than 40?
Bibliographic record
Abstract
OBJECTIVE: The objective of this selective EBM review is to determine whether or not the use of curcumin, versus a placebo, is effective in changing cognitive function. STUDY DESIGN: Review of 3 randomized control trials with blinding in English from 2008 to present. DATA SOURCES: Articles were selected from Cochrane and PubMed databases based on relevance to the selected research question and patient-centered outcomes. OUTCOME(S) MEASURED: The outcome measured is cognitive change, evaluated by mean score changes in the Mini Mental Status Examination (MMSE) or the Montreal Cognitive Assessment (MoCA). RESULTS: Two of the studies demonstrated no change in cognitive function measured with the MMSE (Ringman JM, Frautschy SA, Teng E, et al. Alzheimers Res Ther. 2012;4(5):43. doi: 10.1186/alzrt146. and Baum, L, Lam CW, Cheung SK, et al. J Clin Psychopharmocol. 2008;28(1):110-113. doi: 10.1097/jcp0b013e318160862c), while the third article also did not demonstrate a change in cognitive function, as measured by the MoCA (Rainey-Smith SR, Brown BM, Sohrabi HR, et al. Br J Nutr. 2016;115(12):2106-2113. doi: 10.1017/S0007114516001203). CONCLUSIONS: The studies are unable to demonstrate that curcumin is more effective than placebo in preventing cognitive changes.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".