„Mild-behavioral-impairment“-Checkliste
Bibliographic record
Abstract
BACKGROUND: The mild behavioral impairment (MBI) syndrome is defined by the emergence in later life of persistent neuropsychiatric symptoms. The MBI checklist (MBI-C) can be used for systematic detection and documentation of such symptoms. OBJECTIVE: Development of a German version of the MBI‑C and assessment of its application in a clinical setting. MATERIAL AND METHODS: The MBI‑C was translated from English into German in collaboration with the main author of the original version, and its practical application was then tested on a study population (n = 21) in a gerontopsychiatric inpatient clinic. Patient compliance, understanding of questions, time effort, evaluation procedure and possible discrepancy between patient and family member evaluations were assessed. RESULTS: The German translation of the original MBI‑C obtained certification as an official version and can be downloaded at https://mbitest.org . All 34 questions were fully completed by the study population, the level of understanding of questions was good, with the mean time effort being 16 min. In some cases, significant differences between patients' and family members' responses were found. DISCUSSION: The presence of MBI may indicate the development of an otherwise presymptomatic neurodegenerative dementia syndrome. Hence, the MBI‑C could aid in the early detection of neurodegenerative dementia. By means of the translated version of the MBI‑C presented in this study, this hypothesis can now be tested in German-speaking countries.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".