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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".