Effectiveness of patient decision aids for cardiovascular decisions: systematic review with sex/gender-based analysis
Bibliographic record
Abstract
AIMS: Determine effectiveness of cardiovascular patient decision aids (PtDAs), assess consideration for sex and/or gender in included trials, and report whether PtDAs included sex/gender information in personal cardiovascular risk calculations, benefits or harms. METHODS AND RESULTS: Systematic review with meta-analysis. Independent reviewers screened 209 trials in the 2024 Cochrane Review of PtDAs for eligible cardiovascular trials with updated search to February 2025. Primary outcomes: attributes of the decision-quality and decision-making process. We conducted meta-analysis for similarly measured outcomes. We assessed sex/gender considerations according to International Committee of Medical Journal Editors' recommendations. Thirty-two trials evaluated PtDAs vs. usual care on cardiovascular screening (n = 3 trials; 9.4%), prevention (n = 4; 12.5%), and treatment (n = 25; 78.1%) decisions. There was no difference between groups on decision quality (2 trials). Patients exposed to PtDAs had significantly improved decision-making process outcomes: 12% greater knowledge (20 trials), 127% more accurate risk perceptions (7 trials), 10% feel less uninformed (12 trials), 8% less unclear values (12 trials), and 31% less clinician-controlled decision-making (4 trials). There were no harms. All 32 trials reported sex or gender with 15 (47%) using appropriate terms. One trial reported outcomes separately by sex, but not by study arm. Six (19%) discussed influence of sex/gender on trial findings. Fourteen (43.8%) PtDAs included sex/gender personalized cardiovascular risk scores. CONCLUSION: Cardiovascular PtDAs improve quality of the decision-making process. Less than half of trials used appropriate sex/gender terms and only one reported findings separately by sex/gender. Future PtDA research must improve sex and gender-based reporting and analysis.
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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 | 0.028 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".