Correlation of Depression and Anxiety Responses on the Mild Behavioral Impairment Checklist With the Cornell Scale for Depression in Dementia and Abbreviated Version of the Penn State Worry Questionnaire
Bibliographic record
Abstract
INTRODUCTION: Cognitive disorders are often accompanied by depression and anxiety. The Mild Behavioral Impairment Checklist (MBI-C) was developed to capture neuropsychiatric symptoms that predict risk for dementia and includes questions on mood, but has not been validated for identifying significant depression or anxiety symptoms. Our objective was to determine whether MBI-C mood domain scores predict responses on 2 previously validated scales: the Cornell Scale for Depression in Dementia (CSDD) and the Penn State Worry Questionnaire-Abbreviated version (PSWQ-A) scales. METHODS: We performed a cross-sectional analysis of consenting patients from a memory clinic who completed the MBI-C along with the CSDD (n=80) or PSWQ-A (n=92). RESULTS: MBI-C mood scores and the MBI-C depression subscore were moderately to strongly correlated with the CSDD (r=0.72) and the PSWQ-A (r=0.66). An MBI-C mood score of ≥5 or anxiety or depression subscore ≥2 predicted clinically relevant depressive and anxiety symptoms on the CSDD and PSWQ, respectively, with AUCs between 0.80 and 0.85. CONCLUSIONS: This study supports the MBI-C mood score as a valid tool for screening for mood-related neuropsychiatric symptoms in individuals with cognitive impairment.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".