Decisional Attributes of Patients With Diabetes
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine personal characteristics and preferences that affect decision making (decisional attributes) in patients with diabetes. In particular, we were interested in relating these attributes to the choice of using aspirin to reduce cardiovascular risk. RESEARCH DESIGN AND METHODS: We conducted a cross-sectional survey (70% response rate) of 206 diabetic patients (median age, 63 years; 42% women; 91% completed high school; median HbA(1c), 8%) attending a tertiary care diabetes clinic. Patients answered a 42-question survey exploring decisional attributes. Medical records provided the source of clinical information. We evaluated sociodemographic, clinical, and decisional predictors of aspirin use. We also conducted a multivariable analysis with aspirin use as a dependent variable. RESULTS: Sixty-seven percent of patients surveyed used aspirin. Patients using aspirin were at higher risk of cardiovascular disease (odds ratio 1.4, 95% CI 1.0-2.1), knew more about the benefits of aspirin (1.9, 1.4-2.6) and less about the risks of aspirin (1.4, 1.2-1.8), and were more certain about using aspirin (0.5, 0.3-0.8) than patients not using aspirin. Patients using aspirin placed a higher value on preventing cardiovascular events than on avoiding the side effects of aspirin. Patients perceived that their diabetes provider and the American Diabetes Association had greater influence on their decision to use aspirin than family members or other patients with diabetes. CONCLUSIONS: The decisional attributes of patients with diabetes are associated with aspirin use. Decisional attributes may be the target of research and interventions to reduce underutilization to levels consistent with patient preferences.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".