Usefulness of the Japanese version of Rapid Dementia Screening Test for mild cognitive impairment in older patients with cardiovascular disease: a cross-sectional study.
Bibliographic record
Abstract
BACKGROUND: Cognitive decline is common among older patients with cardiovascular disease (CVD) and can decrease their self-management abilities. However, the instruments for identifying mild cognitive impairment (MCI) are not always feasible in clinical practice. Therefore, this study evaluated whether MCI could be detected using the Japanese version of the Rapid Dementia Screening Test (RDST-J), which is a simple screening tool for identifying cognitive decline. METHODS: This retrospective single-center study included patients who were ≥ 65 years old and hospitalized because of CVD. Patients with a pre-hospitalization diagnosis of dementia were excluded. Each patient's cognitive function had been measured at discharge using the RDST-J and the Japanese version of the Montreal Cognitive Assessment (MoCA-J), which is a standard tool for MCI screening. The correlation between the two scores was evaluated using Spearman's rank correlation coefficient. Receiver operating characteristic (ROC) analysis was also to evaluate whether the RDST-J could identify MCI, which was defined as a MoCA-J score of ≤ 25 points. RESULTS: < 0.001). The ROC analysis revealed that an RDST-J score of ≤ 9 points provided 75.4% sensitivity and 95.2% specificity for identifying MCI, with an area under the curve of 0.899 (95% CI: 0.835-0.964). The same cut-off value was identified when excluding patients with a high probability of dementia (RDST-J score of ≤ 4 points). CONCLUSIONS: The RDST-J may be a simple and effective tool for identifying MCI in older patients with CVD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".