Relationship between Loneliness and Mild Behavioral Impairment: Validation of the Japanese Version of the MBI Checklist and a Cross-Sectional Study
Bibliographic record
Abstract
Background: Mild behavioral impairment (MBI) and loneliness are associated with cognitive decline and an increased risk of dementia. Objective: Our aim was to examine the validity of the Japanese version of the MBI checklist (MBI-C) and investigate the relationship between loneliness and MBI. Methods: The participants in this cross-sectional study included 5 cognitively normal persons and 75 persons with mild cognitive impairment. MBI-C and the revised University of California at Los Angeles loneliness scale (LS) were used to assess MBI and loneliness, respectively. Diagnostic performance of MBI-C was examined using receiver operating characteristic analysis. The relationship between MBI-C and LS was examined using multiple linear regression in 67 subjects who were assessed with both scales, with MBI-C total or domain score as the dependent variable and LS as the independent variable, adjusted for age, gender, living situation, presence of visual and hearing impairment, and Mini-Mental State Examination score. Results: Per the Youden index, in this mostly MCI sample, the optimal MBI-C cut-off score was 5.5 with sensitivity 0.917 and specificity 0.949. In multiple linear regression analysis, LS score was detected as a significant predictor of MBI-C total scores, and MBI-C decreased motivation, affective dysregulation, and abnormal thought and perception scores. Conclusions: The caregiver-rated Japanese MBI-C has excellent diagnostic performance. Loneliness is associated with a greater MBI burden, especially in the decreased motivation, affective dysregulation, and abnormal thought and perception domains. Interventions for loneliness in older people may have the potential to improve MBI.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".