Accuracy of the Cognitive Assessment Battery in a Primary Care Population
Bibliographic record
Abstract
BACKGROUND: There are several cognitive assessment tools used in primary care, e.g., the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment. The Cognitive Assessment Battery (CAB) was introduced as a sensitive tool to detect cognitive decline in primary care. However, primary care validation is lacking. Therefore, we investigated the accuracy of the CAB in a primary care population. OBJECTIVE: To investigate the accuracy of the CAB in a primary care population. METHODS: Data from 46 individuals with cognitive impairment and 33 individuals who visited the primary care with somatic noncognitive symptoms were analyzed. They were investigated with the MMSE, the CAB, and a battery of neuropsychological tests; they also underwent consultation with a geriatric specialist. The accuracy of the CAB was assessed using c-statistics and the area under the receiver operating characteristic curve (AUC) was used to quantify the binary outcomes ("no cognitive impairment" or "cognitive impairment"). RESULTS: The "cognitive impairment" group was significantly different from the unimpaired group for all the subtests of the CAB. When accuracy was based on binary significant reduction or not in one or several domains of the CAB, the AUC varied between 0.685 and 0.772. However, when a summation or logistic regression of several subcategories was performed, using the numerical values for each subcategory, the AUC was >0.9. For comparison, the AUC for the MMSE was 0.849. CONCLUSIONS: The accuracy of the CAB in a primary care population is poor to good when using binary cutoffs. Accuracy can be improved to high when using a summation or logistic regression of the numerical data of the subcategories. Considering CAB time, lack of adequate age norms, and a good accuracy for the MMSE, implementation of the CAB in primary care is not recommended at present based on the results of this study.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".