Diagnostic accuracy of the Brief Assessment of Impaired Cognition case‐finding instrument in a general practice setting and comparison with other widely used brief cognitive tests—a cross‐validation study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The aim of this study was to examine the discriminative validity of the Brief Assessment of Impaired Cognition (BASIC) case-finding instrument in a general practice (GP) setting and compare it with other widely used brief cognitive instruments. METHODS: Patients aged ≥70 years were prospectively recruited from 14 Danish GP clinics. Participants were classified as having either normal cognition (n = 154) or cognitive impairment (n = 101) based on neuropsychological test performance, reported instrumental activities of daily living, and concern regarding memory decline. Comparisons involved the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), the Rowland Universal Dementia Assessment Scale (RUDAS), the Mini-Cog, the 6-item Clock Drawing Test (CDT-6) and the BASIC Questionnaire (BASIC-Q). RESULTS: BASIC demonstrated good overall classification accuracy with an area under the receiver operating characteristic curve (AUC) of 0.88 (95% confidence interval [CI] 0.84-0.92), a sensitivity of 0.72 (95% CI 0.62-0.80) and a specificity of 0.86 (95% CI 0.79-0.91). Pairwise comparisons of the AUCs of BASIC, MMSE, MoCA and RUDAS produced non-significant results, but BASIC had significantly higher classification accuracy than Mini-Cog, BASIC-Q and CDT-6. Depending on the pretest probability of cognitive impairment, the positive predictive validity of BASIC varied from 0.83 to 0.36, and the negative predictive validity from 0.97 to 0.76. CONCLUSIONS: BASIC demonstrated good discriminative validity in a GP setting. The classification accuracy of BASIC is equivalent to more complex, time-consuming instruments, such as the MMSE, MoCA and RUDAS, and higher than very brief instruments, such as the CDT-6, Mini-Cog and BASIC-Q.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".