Ethnic Differences Between Hispanics and Non-Hispanic Whites in Neuropsychiatric Symptoms Predict Conversion to Mild Cognitive Impairment
Bibliographic record
Abstract
The aim of the study is to ascertain the neuropsychiatric symptoms (NPS) subtypes significantly influencing progression to mild cognitive impairment (MCI) by ethnicity. In this retrospective cohort study, we included 386 cognitively normal individuals participating in the longitudinal Texas Alzheimer's Research and Care Consortium between February 2007 and August 2014. The primary outcome was time to incident MCI. Data driven NPS subtypes at baseline were identified and the effects of these subtypes on the outcome were obtained for Hispanic and non-Hispanic ethnic cohorts and summarized with a hazard ratio (HR). Three NPS subtypes were identified and internally validated: psychomotor apathy factor (including agitation, irritability, apathy), affective mood factor (including depression, anxiety), and physical behavior factor (including nighttime behavior, eating/appetite disturbances). In adjusted analysis, a psychomotor apathy score of NPS was the best predictor for MCI (HR = 2.19, p = 0.037) among non-Hispanics whereas physical behavior score was the most predictive of MCI (HR = 2.55, p = 0.029) among Hispanics. A high score of affective mood factor also tended to increase the risk of MCI (HR = 2.09, p = 0.06) in Hispanics. Progression from normal cognition to MCI was differentially predicted by NPS subtypes in Hispanics and non-Hispanic whites. These data may inform the allocation of efforts for monitoring individuals at-risk of MCI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".