PREVALENCE OF METABOLIC COMORBIDITIES AMONG COGNITIVELY NORMAL AND IMPAIRED WHITE AND AFRICAN AMERICANS
Bibliographic record
Abstract
Abstract Assessing comorbidities associated with a MCI diagnosis is crucial for diagnostic accuracy and for understanding the role of comorbidities in cognitive decline. In this study of amnestic (aMCI) and non-amnestic (naMCI) MCI participants and persons with normal cognition (CN), we compared the prevalence of three primary comorbidities: Hypertension (HTN), Hyperlipidemia (HLD), and Diabetes Mellitus (DM) across African American and White populations using Chi-squares. Participant data (N = 9,342, 17% African American) were available through the National Alzheimer’s Coordinating Center and included: CN (n = 5,963; MOCA mean=27), aMCI (2,694; MOCA=22), and naMCI (685; MOCA=24) with diagnosis and data per their first Uniform Data Set (Version 3) visit. Significant differences in the distribution of HTN, HLD, and DM were found among the diagnostic groups for the total cohort and racial groups, separately; however, diagnostic differences across races were not always consistent. The relative rates of DM and HLD across the diagnostic groups for both races were generally similar, though higher percentages were seen in African Americans (25% of African Americans, 10% of Whites). As for HTN (52% of African Americans, 39% of Whites), however, the distributions differ across the diagnostic groups and race (%yes for diagnosis; White Americans: CN 35%, aMCI 47%, naMCI 44%; African Americans: CN 66%, aMCI 70%, naMCI 79%, p< 0.001). These findings highlight the importance of considering the contributions of both race and diagnosis when evaluating the role of comorbid factors and metabolic disorders in NC and MCI groups, in particular when considering blood pressure-related measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".