A-251 A Community-Based Longitudinal Study of Mild Cognitive Impairment (MCI) in Georgia
Bibliographic record
Abstract
Abstract Objective: As the average population ages, dementia has become one a leading challenge for health care systems in developing and developed countries. Dementia is a progressive disorder associated with the decline of cognitive function and the ability to carry out daily activities. Along with the development of screening tools to detect early-stage MCI, it is important to find out the time-line for the MCI progression. There is little recent information available on the natural course of MCI in Georgia. Method: A 7-year longitudinal study was conducted to track MCI progression, comparing individuals with initially healthy cognition (N=52) to patients showing symptoms of MCI (N=51). This study used MoCA as an indicator of cognitive change over the 7-year period. The MoCA was administered twice approximately 7 years apart and IADLs-at the end of the research. Participants were classified as MCI or cognitively intact based, on the results of the MoCA results. Results: Healthy individuals had a very limited decline in MoCA scores (M=-0.004, p<.001); MCI group also showed a some decrease in MoCA scores (M=-1.2, p<.002) without any changes in IADLs scores; whereas 17.6% of healthy individuals progressed to MCI at the end of the research (M=- 4.8, p<.03), associated with the significant decline of the IADLs scores (M=-2, St.dev=3.2, p<.03), as well. Conclusion: Progression of MCI based on MoCA and IADLs results can indicate patients’ declining ability for self-care. Because MCI patients will need increased care and monitoring as the disease progresses, the social burden of caring for these patients will likely also increase.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".