How Well Does the Brief Interview for Mental Status Identify Risk for Cognition Mediated Functional Impairment in a Community Sample?
Bibliographic record
Abstract
OBJECTIVE: To determine the adequacy of the Brief Interview for Mental Status (BIMS) compared with other screening tools in identifying individuals with limitations in functional cognition and instrumental activities of daily living (IADL). DESIGN: Cross-sectional observational study. SETTING: Midsized midwestern city. PARTICIPANTS: We assessed a convenience sample of community dwelling individuals (N=197) aged 55 years and older who were living independently. MAIN OUTCOME MEASURES: Participant scores on the BIMS, Mini-Cog, Menu Task, and Montreal Cognitive Assessment (MoCA) were compared with the Performance Assessment of Self-Care Skills Checkbook Balancing and Shopping tasks (PCST), which are known to predict impairment in complex IADLs associated with a diagnosis of mild cognitive impairment. Multiple logistic regression analyses controlling for participant demographics, as well as sensitivity and specificity, were computed for each screening measure using the PCST as the criterion measure. RESULTS: The Mini-Cog, Menu Task, and MoCA identified 25.89%, 32.49%, and 47.21% more individuals, respectively, as impaired than the BIMS. In multiple logistical regression analyses, the BIMS correctly identified 58% of those impaired on the PCST. However, each of the alternate screening measures correctly identified at least 70% of individuals as impaired on the PCST. CONCLUSIONS: In this community sample, the BIMS was insensitive to subtle impairments with the potential to compromise community living, suggesting that the BIMS may be inappropriate for use outside nursing home 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
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".