What Is the Effectiveness of Type 2 Diabetes–related Patient Decision Aids? Secondary Analysis of a Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Patient decision aids (PtDAs) are evidence-based interventions to help people faced with difficult health-care decisions. Little is known about their effectiveness in people facing diabetes-related decisions. The aim of this study was to evaluate the scope and effectiveness of diabetes-related PtDAs for screening, prevention, and treatment decisions. METHODS: A secondary analysis of randomized controlled studies (RCTs) from the 2024 Cochrane review of PtDAs comparing decision aids on diabetes screening, prevention, or treatment to usual care (e.g. patient education, no intervention) was conducted. Two reviewers independently screened citations, extracted data, and assessed study quality. Primary outcomes included quality of the decision and decision-making process. Meta-analyses were conducted for similar outcome measures. RESULTS: Of the 209 RCTs, 11 eligible studies evaluated diabetes PtDAs for treatment (n=7), screening (n=3), and prevention (n=1). Common decisions were about diabetes treatment intensification (n=4) and statin initiation (n=3) in people with type 2 diabetes. Compared with usual care, the PtDA group reported increased knowledge (mean difference [MD] 16.06, 95% confidence interval [CI] 8.38 to 23.75) and clearer values (MD -7.43, 95% CI -13.23 to -1.63) and no difference in accurate risk perceptions. After removing high-risk-of-bias studies, PtDAs led to fewer patients feeling uninformed about their options (MD -6.38, 95% CI -9.58 to -3.19) and more participants starting new medications (relative risk ratio 1.65, 95% CI 1.06 to 2.56). Six studies measured adherence to a chosen option: 1 reported greater adherence, whereas another reported lower adherence in PtDA vs usual care and the remaining 4 reported no difference. CONCLUSIONS: Patients given PtDAs can improve their knowledge and feel informed and clearer about their values while being more likely to start new medications. Future research can strengthen the certainty of these findings and should explore PtDA use within the chronic disease context.
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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 | 0.024 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".