Mild Behavioral Impairment and Plasma Biomarkers of Neurodegeneration as Predictors of Cognitive Decline in Geriatric Patients With Psychiatric Disorders
Bibliographic record
Abstract
Background Mild Behavioral Impairment (MBI) has been increasingly recognized as a potential early clinical marker of neurodegenerative disease, while blood-based biomarkers such as phosphorylated tau 217 (p-tau217) and neurofilament light chain (NfL) are associated with Alzheimer’s disease and axonal damage, respectively. Objective To investigate the role of MBI and blood-based biomarkers of neurodegeneration in the early detection of dementia. Methods Fifty-one individuals without dementia aged 60 or older with mood or anxiety disorders underwent psychiatric, neuropsychiatric, and cognitive evaluations, as well as assessment of plasma p-tau217 and NfL at baseline and at one-year follow-up. Results A higher proportion of males was observed in the MBI group compared to the non-MBI group ( P = 0.076). MBI was significantly associated with a higher risk of conversion to dementia ( P = 0.006). MBI patients showed a trend toward higher baseline p-tau217 ( P = 0.096) and significantly higher NfL at follow-up ( P = 0.025), suggesting active neurodegeneration. Individuals who converted to dementia had marginally higher baseline p-tau217 ( P = 0.053) and NfL ( P = 0.091). Conclusion MBI and blood-based biomarkers of neurodegeneration appear to be promising clinical tools for identifying dementia risk in its early stages.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".