Effectiveness of shared goal setting and decision making to achieve treatment targets in type 2 diabetes patients: A cluster‐randomized trial (<scp>OPTIMAL</scp>)
Bibliographic record
Abstract
OBJECTIVE: About 20% of patients with type 2 diabetes achieve all their treatment targets. Shared decision making (SDM) using a support aid based on the 5-years results of the ADDITION study on multifactorial treatment, could increase this proportion. RESEARCH DESIGN AND METHODS: Cluster-randomized trial in 35 former ADDITION primary care practices. Practices were randomized to SDM or care as usual (1:1). Both ADDITION and non-ADDITION type 2 diabetes patients, 60-80 years, known with diabetes for 8-12 years, were included. In the intervention group, patients were presented evidence about the relationship between treatment intensity and cardiovascular events. They chose intensive or less intensive treatment and prioritized their targets. After 1 year priorities could be rearranged. FOLLOW-UP: 24 months. Intention-to-treat analysis. MAIN OUTCOME MEASURE: proportion of patients that achieved all three treatment targets. RESULTS: At baseline 26.4% in the SDM group (n=72) had already achieved all three treatment goals (CG: 23.5%, n=81). In the SDM group 44 patients chose intensive treatment, 25 continued their former less intensive treatment and three people switched from the more to the less intensive protocol. After 24 months 31.8% of the patients in the SDM group achieved all three treatment targets (CG: 25.3%), RR 1.26 (95% CI 0.81-1.95). Mean systolic blood pressure decreased in the SDM group (-5.4 mm Hg, P<.01), mean HbA1c and total cholesterol did not change. CONCLUSIONS: Despite an already high baseline level of diabetes care, we found strong indications that SDM on both intensity of treatment and prioritizing treatment goals further improved outcomes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".