Vascular risk and neuroimaging profile of MCI patients in the FINOMAIN Study
Bibliographic record
Abstract
Abstract Background The Asian population has been found to be at a higher risk of developing dementia compared to other races due to the high prevalence of vascular risk factors. Utilization of different modalities that manage these vascular risk factors is the focus of this study in order to prevent progression of dementia among senior citizens with mild cognitive impairment. Method Community‐based participants with MCI were recruited to join the FINOMAIN Study. They were divided into control and intervention groups, to which INDAK (dance therapy) shall be given. Both groups shall receive health education, management of non‐communicable diseases, and nutrition management. 72 participants shall undergo neuroimaging. Hypertension, diabetes mellitus type 2, and dyslipidemia were derived from clinical data and ancillary procedures. Small vessel disease markers (white matter hyperintensity, chronic lacunes, enlarged perivascular spaces, global atrophy, medial temporal lobe atrophy, frontotemporal atrophy, and posterior cortical atrophy) were documented through T1W, T2W, and FLAIR sequences on MRI and with the use scales like Fazekas, modified Staal’s score, and atrophy scores. All MRI assessments were performed by a single neurologist blinded to the clinical and cognitive measures. Cognitive measures had been done including tests for global cognition (MMSE‐P and MoCA‐P). Independent t‐test was used to show descriptive results of both groups. Result 42 subjects had completed MRI as of this writing (18 control group, 24 intervention group) with a mean age of 70 and mean years of education 9.48 years. Only 12% of the subpopulation are males. Markers of noncommunicable diseases did not show significant differences between groups. SVD burden measured through modified Staal’s score showed 2.50+0.985 for the control group and 2.33+0.917 for the intervention group (with a maximum score of 3 points) Fazekas scores for both groups showed moderate WMH burden (2.17+0.786 vs. 2.29+0.859; p = 0.125). MTA scores did not exceed the score of 2 in both groups (1.56+0.784 vs. 1.50+0.590; p = 0.794). Conclusion The subsample of the FINOMAIN study shows comparable demographics, clinical, and neuroimaging findings which is considered as a good pre‐intervention data. Small vessel disease burden in the neuroimaging is quite high in both study groups.
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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 | 0.001 | 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".