Mild Behavioral Impairment and Subjective Cognitive Decline predict Mild Cognitive Impairment
Bibliographic record
Abstract
Abstract Objective Better methods for detecting preclinical neuropathological change are required for prevention of dementia. Mild behavioral impairment (MBI) and subjective cognitive decline (SCD) can represent neurobehavioral and neurocognitive axes of early stage neurodegenerative processes, which are represented in Stage 2 of the NIA-AA Alzheimer’s disease research framework. Both MBI and SCD may offer an opportunity for premorbid detection. We test the hypothesis that MBI and SCD confer additive risk for incident cognitive decline. Methods Participants were cognitively normal older adults followed up approximately annually at Alzheimer’s Disease Centers. Logistic regression was used to determine the relationship between baseline classification (MBI+, SCD+, neither (MBI-SCD-), or both (MBI+SCD+)) and cognitive decline, defined by Clinical Dementia Rating (CDR) total score, at 3 years. Results Of 2769 participants (mean age=76; 63% females), 1536 were MBI-SCD-, 254 MBI-SCD+, 743 MBI+SCD-, and 236 MBI+SCD+. At 3-years, 349 individuals (12.6%) developed cognitive decline to CDR>0. Compared to SCD-MBI-, we observed an ordinal progression in risk, with ORs [95% CI] as follows: 3.61 [2.42-5.38] for MBI-SCD+ (16.5% progression), 4.76 [3.57-6.34] for MBI+SCD-, (20.7% progression) and 8.15 [5.71-11.64] for MBI+SCD+ (30.9% progression). Conclusion MBI in older adults alone or in combination with SCD is associated with a higher risk of incident cognitive decline at 3 years. The highest rate of progression to MCI is observed in those with both MBI and SCD. Used in conjunction, MBI and SCD could be simple and scalable methods to identify patients at high risk for cognitive decline for prevention studies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".