616-P: Self-Care Scores Predicting Cognition Status in Patients with Type 2 Diabetes
Bibliographic record
Abstract
Cognitive impairment is common in patients with type 2 diabetes mellitus (T2DM) and negatively impacts effective diabetes self-care. However, it is unclear whether self-care level can predict cognition status in T2DM patients. We aimed to identify self-care scores that would provide information on the patients’ cognition status. Methods: We collected diabetes self-care and cognition data from 50 T2DM patients (age 55.9±7.6 years, diabetes duration 10.1±8.2 years, A1C 7.1±1.4%, 54% female) using the Scale for Summary of Diabetes Self-Care Activities Assessment (SDSCA) and the Montreal Cognitive Assessment (MoCA) . A mixed effects logistic regression model for repeated measures used both baseline and 6 months follow-up observations with adjustment for within-subject correlation (SAS GLMMIX) . First, from nine potential covariates, an iterative backward elimination process selected a parsimonious subset of covariates for predicting MoCA level (normal vs. impairment) , optimizing area under the curve (AUC) . Four covariates were identified (education, income, ethnicity, sleep quality; AUC=.75) . Then an iterative process calculated AUC for MoCA level predicted from self-care level, using each possible self-care score cut point from .to 6.95 in .increments, controlling for the four identified covariates. An optimal cut point was selected that produced the highest AUC value. Results: The optimal self-care cut point was 2.95 (AUC=.76) , producing good discrimination of MoCA level. As a sensitivity analysis, the second best cut point of 3.95 produced AUC=.75. Conclusion: Self-care cut points, accounting for education, income, ethnicity, and sleep quality, could be used to gain information on T2DM patients’ cognition status, which may prompt clinicians and researchers to further evaluation with formal cognitive tests. Disclosure S.E.Choi: None. M.Freeby: Research Support; Abbott Diabetes, Novo Nordisk. B.Roy: None. M.A.Woo: None. R.Kumar: None. M.Brecht: None. Funding National Institutes of Health (RNR017190)
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".