Assessing mild behavioral impairment with the mild behavioral impairment checklist in people with subjective cognitive decline
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACTObjectives:To estimate the prevalence of Mild Behavioral Impairment (MBI) in people with Subjective Cognitive Decline (SCD), and validate the Mild Behavioral Impairment Checklist (MBI-C) with respect to score distribution, sensitivity, specificity, and utility for MBI diagnosis, as well as correlation with other neuropsychological tests. DESIGN: Correlational study with a convenience sampling. Descriptive, logistic regression, ROC curve, and bivariate correlations analyses were performed. SETTING: Primary care health centers. PARTICIPANTS: 127 patients with SCD. MEASUREMENTS: An extensive evaluation, including Questionnaire for Subjective Memory Complaints, Mini-Mental State Examination, Cambridge Cognitive Assessment-Revised, Neuropsychiatric Inventory-Questionnaire (NPI-Q), the Geriatric Depression Scale-15 items (GDS-15), the Lawton and Brody Index and the MBI-C, which was administered by phone to participants' informants. RESULTS: MBI prevalence was 5.8% in those with SCD. The total MBI-C scoring was low and differentiated people with MBI at a cut-off point of 8.5 (optimizing sensitivity and specificity). MBI-C total scoring correlated positively with NPI-Q, Questionnaire for Subjective Cognitive Complaints (QSCC) from the informant and GDS-15. CONCLUSIONS: The phone administration of the MBI-C is useful for detecting MBI in people with SCD. The prevalence of MBI in SCD was low. The MBI-C detected subtle Neuropsychiatric symptoms (NPS) that were correlated with scores on the NPI-Q, depressive symptomatology (GDS-15), and memory performance perceived by their relatives (QSCC). Next steps are to determine the predictive utility of MBI in SCD, and its relation to incident cognitive decline over time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 it