Validity and Reliability of the Turkish Version of Mild Behavioral Impairment Checklist in Patients With Cognitive Impairment
Bibliographic record
Abstract
Background and Objective The Mild Behavioral Impairment-Checklist (MBI-C) was developed to detect and standardize neuropsychiatric symptoms. The objective of this study was to evaluate the Turkish adaptation, validity, and reliability of the MBI-C. Methods The sample of our study consisted of 80 patients with cognitive impairment and a control group with 113 participants whose cognitive impairment was not detected in standard tests. Participants were evaluated with the Standardized Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale-15 (GDS-15), MBI-C and Neuropsychiatric Inventory (NPI). Results and Conclusion In the reliability analysis, the Cronbach-alpha value for MBI-C was found to be .810. In the ROC analysis performed with the total MBI-C score, the area under the curve (AUC) was calculated as .821 and the cut-off score was determined as 8.5; sensitivity was calculated as .77 and specificity as .83. A strong positive correlation was found between test-retest MBI-C scores (r = .886, P < .0019). A strong positive correlation was found between MBI-C and NPI scores (r = .964, P < .001). MBI-C scores were significantly negatively correlated with MMSE and MoCA scores and positively correlated with GDS-15 scores. The results of our study showed that the Turkish version of the MBI-C is a valid and reliable measurement.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".