Applying Patient Segmentation Using Primary Care Electronic Medical Records to Develop a Virtual Peer-to-Peer Intervention for Patients with Type 2 Diabetes
Why this work is in the frame
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Bibliographic record
Abstract
The aim of this study was to design a virtual peer-to-peer intervention for patients with type 2 diabetes (T2D) by grouping patients from specific segments using data from primary care electronic medical records (EMRs). Two opposing segments were identified: patients living with diabetes who tend to take several medications (“medication” segment: ~32%) and patients who do not take any diabetes-specific medications (“lifestyle” segment: ~15%). The remaining patients were from two intermediate segments and exhibited medication-taking behavior that placed them midway between the medication and lifestyle segments. Patients were grouped into six workshops (two workshops in each group: medication, lifestyle, and mixed group), including individuals with good and bad control of their disease. Measures of attitudes, learning, and motivation were addressed during and after the workshops. Results showed that patients in the lifestyle segment were more interested in T2D lifestyle control strategies, more satisfied with their in-workshop learning experience, and more motivated to set a goal than those in the medication segment. These results suggest that the proposed intervention may be more viable for patients in the lifestyle segment and that EMR data may be used to tailor behavioral interventions to specific patient groups. Future research is needed to investigate different segmentation approaches (e.g., using data related to smoking, drinking, diet, and physical activity) that could help tailor the intervention more effectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it