Usefulness of the Cognitive Composition Test as an Early Discriminator of Mild Cognitive Impairment
Bibliographic record
Abstract
Mild cognitive impairment (MCI) is the preliminary stage of dementia, which is a serious social problem worldwide. This study aimed to investigate whether the Cognitive Composition Test (CCT) is effective for the early diagnosis of MCI. A total of 104 older adults underwent the Montreal Cognitive Assessment (MoCA), the Mini-Mental State Examination (MMSE), the Trail Making Test Parts A (TMT-A) and B (TMT-B), and our newly prototyped cognitive composition test (CCT). We created three types of CCT (CCT-A, CCT-B, and CCT-C) with different degrees of difficulty. First, we examined the concurrent validity of CCT-A, CCT-B, and CCT-C with the MoCA, MMSE, TMT-A, and TMT-B. All participants were classified into the healthy control (HC) and MCI groups based on their scores in the Japanese versions of the MoCA and MMSE. The HC and MCI groups were compared using the TMT-A, TMT-B, CCT-A, CCT-B, and CCT-C. Finally, we examined the sensitivity for discrimination of CCT-C. CCT-C had a higher discrimination sensitivity than TMT-A, TMT-B, CCT-A, and CCT-B, with a cut-off value of 65.75 s, a sensitivity level of 0.844, and a specificity of 0.776. It may be a useful screening tool for the early diagnosis of the early-stages of dementia, such as MCI, in asymptomatic older adults.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".