Impact of Mild Behavioral Impairment on Longitudinal Changes in Cognition
Bibliographic record
Abstract
BACKGROUND: To examine cross-sectional differences and longitudinal changes in cognitive performance based on the presence of mild behavioral impairment (MBI) among older adults who are cognitively healthy or have mild cognitive impairment (MCI). METHODS: Secondary data analysis of participants (n = 17 291) who were cognitively healthy (n = 11 771) or diagnosed with MCI (n = 5 520) from the National Alzheimer's Coordinating Center database. Overall, 24.7% of the sample met the criteria for MBI. Cognition was examined through a neuropsychological battery that assessed attention, episodic memory, executive function, language, visuospatial ability, and processing speed. RESULTS: Older adults with MBI, regardless of whether they were cognitively healthy or diagnosed with MCI, performed significantly worse at baseline on tasks for attention, episodic memory, executive function, language, and processing speed and exhibited greater longitudinal declines on tasks of attention, episodic memory, language, and processing speed. Cognitively healthy older adults with MBI performed significantly worse than those who were cognitively healthy without MBI on tasks of visuospatial ability at baseline and on tasks of processing speed across time. Older adults with MCI and MBI performed significantly worse than those with only MCI on executive function at baseline and visuospatial ability and processing speed tasks across time. CONCLUSIONS: This study found evidence that MBI is related to poorer cognitive performance cross-sectionally and longitudinally. Additionally, those with MBI and MCI performed worse across multiple tasks of cognition both cross-sectionally and across time. These results provide support for MBI being uniquely associated with different aspects of cognition.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".