Impact of the Diabetes Shared Care Program on Glycemic Control in Older Adults With Type 2 Diabetes
Bibliographic record
Abstract
Background: This study aimed to assess the impact of the Diabetes Shared Care Program (DSCP) on glycemic control among elderly patients with type 2 diabetes mellitus (T2DM) over 1 year and identify factors associated with A1C level outcomes. Methods: A retrospective cohort study was conducted at a regional hospital in central Taiwan from 2016 to 2020. The study included 509 patients aged ≥ 65 years with a confirmed T2DM diagnosis who participated in the program for at least 1 year. A1C levels were analyzed using three thresholds (6.5%, 7%, and 8%), and sociodemographic and health-related factors were examined. Statistical analyses included paired t-tests, the McNemar test, and binary logistic regression models. Results: After 1 year in the DSCP, the mean A1C level significantly decreased from 7.37 ± 1.30 to 7.11 ± 1.13 (P < 0.001). Glycemic control patterns varied across A1C thresholds, with the most significant improvements observed at the 8% threshold, while improvements were less pronounced at the 6.5% threshold. Abnormal waist circumference was significantly associated with poorer glycemic control, with odds ratios of 2.570 (95% confidence interval (CI): 1.409 - 4.690, P = 0.002) for A1C < 6.5%, 2.360 (95% CI: 1.362 - 4.087, P = 0.002) for A1C < 7%, and 3.169 (95% CI: 1.909 - 5.261, P < 0.001) for A1C < 8%. Conclusions: The DSCP significantly improved glycemic control in elderly patients with T2DM. Targeted diabetes education interventions should be implemented for older adults at higher risk, particularly those with abnormal waist circumference.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".