P1‐516: THE SCREENING FEASIBILITY OF A MILD BEHAVIORAL IMPAIRMENT CHECKLIST IN CHINESE PATIENTS WITH ALZHEIMER'S DISEASE
Bibliographic record
Abstract
The mild behavioral impairment checklist (MBI-C) is a screening scale tailored to evaluate neuropsychiatric symptoms of patients with pre-dementia. Meanwhile, Alzheimer's disease (AD) as the most common type of dementia, is also often marked by neuropsychiatric symptoms, making it possible to use MBI-C for screening. However, the screening feasibility of MBI-C in AD population still need to be verified. To calculated the reliability and validity of MBI-C, as well as compared its sensitivity and specificity with NPI-Q to develop a new AD screening scale suitable for Chinese. MBI-C was translated into Chinese and back translated with the consent of the original author. Forty six patients with Alzheimer's disease (AD) in the memory clinic of Xuanwu hospital and 50 gender and education matched controls were selected from community. All subjects were evaluated by the MBI-C, the neuropsychiatric inventory questionnaire (NPI-Q), the mini-mental state examination (MMSE), the Montreal cognitive assessment (MoCA), the activity of daily life scale (ADL) and the clinical dementia rating scale (CDR). Among them, fifteen patients with AD were evaluated repeatedly after the interval of more than 24h, and eight patients with AD were evaluated simultaneously by 2 evaluators. The Chinese version of the MBI-C demonstrated great internal consistency reliability (Cronbach’α = 0.936), test-retest reliability (Linear correlation coefficient=0.841), and inter-evaluator reliability (Linear correlation coefficient=0.991). The optimal cutoff point of the MBI-C is 6/7 for identifying AD dementia against controls, with a good sensitivity (86.96%) and specificity (86.00%) better than NPI-Q (sensitivity76.09% and specificity76.00%). The Pearson correlation coefficients ranged from 0.702 to 0.831 showing a good content validity. Moreover, the Pearson correlation coefficients is 0.758 to reflect the criterion validity. Meanwhile, MBI-C can also distinguish the severity of AD dementia. Moreover, MBI-C scores were significantly negatively correlated with MMSE and MoCA scores (r=−0.641, P < 0.05;r=−0.623, P < 0.05), positively correlated with ADL (r=0.742, P < 0.05). This study shows that the Chinese version of MBI-C has a high reliability and validity, as well as be more sensitive and specific for screening AD patients compared with NPI-Q. MBI-C is expected to be an effective tool for screening AD in Chinese.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".