P1‐376: A Study of Cognitive Reserve Affecting Performance in Memory Screening Using MMSE and MOCA in Normal Healthy Singaporean Adults
Bibliographic record
Abstract
In commeration of World AD day, free cognitive screening programme was conducted in Singapore General Hospital. 215 participants volunteered to undergo free cognitive screening on that day by prearranged sessions. Two cognitive screening tools were employed in this study: the Mini-Mental State Examination (MMSE; Folstein et al., 1975) and the Montreal Cognitive Assessment (Nasreddine, 2010). Study participants cognitive tests administered by trained nurses and psychology students. Demographic characteristics, such as, age, education, gender, and race were collected. Further analysis was conducted on the effects of education and age on the MoCA total scores and subdomain scores. The MoCA covers a larger variety of cognitive domains than the MMSE. There is more equal distribution in scoring of cognitive domains in the MoCA than in the MMSE. Mean MoCA total scores showed a declining trend both in lower education and increasing age. Significant correlation was found in all cognitive domains, with Orientation and Attention having a stronger correlation. All t-values reached significance level, with the exception of the drawing domain. The scores in the MoCA test showed both educational and age effects in the total scores of the participants. Our findings show that, two screening tests measure similar cognitive domains, as evident from the significant correlations, it is also apparent that there is a difference to the degree of these measurements. In particular, participants generally performed better in the MMSE than in the MoCA screening test, as the mean total score for MMSE is significantly greater. This can be attributed to the MMSE’s ceiling effect, in which participants tend to perform in the higher range of scores. Moreover, the range of scores in MoCA is much wider – with participants scoring as low as 8 points and as high as 30 points.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".