Validation of a new cognitive screening tool, the Brain Health Test‐7, for identification of mild cognitive impairment and early dementia
Bibliographic record
Abstract
Abstract Background The Brain Health Test‐7 (BHT‐7) was developed to identify patients with mild cognitive impairment (MCI) and early dementia. Here we report the validity of the BHT‐7 versus the Mini‐Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in subjects of different psychiatry or neurology clinics. Method Patients with the chief complaint of memory decline were recruited in this study from the outpatient clinic of psychiatry or neurology in 3 different kinds of hospitals between November 2017 to December 2020. All patients underwent the evaluation of the BHT‐7, MMSE, MoCA, and CDR by trained research assistants or board‐certified psychologists. The final clinical diagnosis (normal, MCI, dementia) was made by consensus meeting, taking into account all available data, including clinical history and symptoms, brain image, cognitive tests, and CDR. Demographic data (age, gender, and education level) and the scores of the MMSE, MoCA, and BHT‐7 between groups were compared. Logistic regression was adopted for analysis of optimal cutoff values, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the ROC curve. Result We enrolled a total of 1090 subjects (normal 402, MCI 317, dementia 371); of them, 705 (64.7%) were female. There was a statistically significant difference in age, years of education, and 3 cognitive test scores among the 3 groups of subjects. Compared with the MMSE and MoCA, the BHT‐7 performed slightly better in differentiating MCI and dementia. In sub‐group analysis (divided based on 6 years of education), the BHT‐7 still performed slightly better than MMSE and MoCA. The testing time for the BHT‐7 was about 5‐7 minutes, shorter than that of the MMSE and MoCA. For BHT‐7, the cutoff point was 17 between normal and MCI, and 14 between normal and dementia. These cutoff points of BHT‐7 were consistent through 3 different settings (medical center’s psychiatry and neurology clinics, and outpatient clinic of a psychiatry center). In contrast, the cutoff points were inconsistent for MMSE and MoCA in different settings. Conclusion The results support that BHT‐7 may be a useful cognitive screening tool for MCI or early dementia in various clinic settings.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".