VALIDATION OF THE FINNISH VERSION OF THE MONTREAL COGNITIVE ASSESSMENT TEST1
Bibliographic record
Abstract
The aim of this study was to determine the clinical utility of the Finnish version of the MoCA test for screening Alzheimer’s disease and MCI. The purpose was to examine the ability (sensitivity and specificity) of the MoCA to distinguish patients with AD and MCI from cognitively normal controls. The study population consists of three participant groups: patients with AD (n=25), patients meeting the criteria for MCI (n=18), and cognitively normal controls (NC) (n=39). The AD group consists of subjects with very mild (CDR= 0.5, n=12), mild (CDR=1, n=12), and moderate (CDR=2, n=1) dementia, and they were given a diagnosis of dementia by using the revised NINCDS-ARDRA criteria. The normal control group (NC) consists of 39 cognitively normal volunteer participants. The three study groups differed from each other in terms of sex, age, and level of education. The NCs were younger than the subjects with AD (t [37,374] = 3.265, p = 0.002) and MCI (t [30,800] = 4.306, p = < 0.001). The NCs were also better-educated than the patients with AD (t [54,975] = -3.419, p = 0.001) and MCI (t [40,782] = -3.008, p = 0.004). The sensitivity and specificity of the MoCA in detecting AD and MCI was done according to various cutoff points. With a cutoff score of 26, the MoCA had a sensitivity of 100% to detect subjects with AD and a sensitivity of 100% to detect subjects with MCI. The specificity was 79.5%. With a cutoff score of 24, which was the best threshold in the present study, the MoCA not only had a high sensitivity to detect subjects with AD (96%) and MCI (89%) but also delivered a high specificity (97%). The MoCA has a high sensitivity and specificity to detect subjects with AD and MCI with a cutoff score of 24/30. The Finnish version of the MoCA is a feasible screening instrument for assessing cognitive decline. According to our study, the optimal cutoff score of the MoCA is 24/30.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".