Performance of the Brain Health Test-7, Mini-Mental State Examination, and Montreal Cognitive Assessment for detecting subjects with mild cognitive impairment
Bibliographic record
Abstract
OBJECTIVES: The Brain Health Test-7 (BHT-7), Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA) are valid dementia and mild cognitive impairment (MCI) screening tools. Relevant validation studies have usually used receiver operating characteristic (ROC) curve analysis or a fixed-threshold approach. In this study, we adopted stratum-specific likelihood ratio (SSLR) analysis to capture more information about their performance in detecting MCI. DESIGN: Cross-sectional multi-site study. SETTING: Hospitals in northern and southern Taiwan. PARTICIPANTS: 1090 subjects aged 50 years or older were assigned to a cognitively normal group, an MCI group, or a dementia group. MEASUREMENTS: BHT-7, MMSE, and MoCA to differentiate cognitively normal subjects from those with MCI or dementia. RESULTS: The three cognitive assessment tools were valid for detecting subjects with MCI or dementia according to ROC analysis. The overall area under the ROC curve (AUC) of the BHT-7 was significantly higher than that of the MoCA and MMSE in differentiating MCI or dementia from controls. Five strata were generated by SSLR analysis for the BHT-7 and MoCA, while 4 for the MMSE. The five strata of the BHT-7 and MoCA well represented the different degrees of probabilities of having MCI. However, it was still difficult to rule out the presence of MCI even by a test score within the highest-score stratum of the MMSE. CONCLUSIONS: The BHT-7 performed slightly better than MoCA and MMSE in detecting subjects with MCI. The strata generated from the SSLR analysis were more informative than single cutoff values.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".