A formalized shared decision-making process with individualized decision aids for older patients referred for cardiac surgery
Bibliographic record
Abstract
Background: Comprehension of risks, benefits and alternative treatment options is poor among patients referred for cardiac surgery interventions. We sought to explore the impact of a formalized shared decision-making (SDM) process on patient comprehension and decisional quality among older patients referred for cardiac surgery. Methods: We developed and evaluated a paper-based decision aid for cardiac surgery within the context of a prospective SDM design. Surgeons were trained in SDM through a Web-based program. We acted as decisional coaches, going through the decision aids with the patients and their families, and remaining available for consultation. Patients (aged ≥ 65 yr) undergoing isolated valve, coronary artery bypass graft (CABG) or CABG and valve surgery were eligible. Participants in the non-SDM phase followed standard care. Participants in the SDM group received a decision aid following cardiac catheterization, populated with individualized risk assessment, personal profile and comorbidity status. Both groups were assessed before surgery on comprehension, decisional conflict, decisional quality, anxiety and depression. Results: We included 98 patients in the SDM group and 97 in the non-SDM group. Patients who received decision aids through a formalized SDM approach scored higher in comprehension (median 15.0, interquartile range [IQR] 12.0–18.0) than those who did not (median 9.0, IQR 7.0–12.0, p < 0.001). Decisional quality was greater in the SDM group (median 82.0, IQR 73.0–91.0) than in the non-SDM group (median 76.0, IQR 62.0–82.0, p < 0.05). Decisional conflict scores were lower in the SDM group (mean 1.76, standard deviation [SD] 1.14) than in the non-SDM group (mean 5.26, SD 1.02, p < 0.05). Anxiety and depression scores showed no significant difference between groups. Conclusion: Institution of a formalized SDM process including individualized decision aids improved comprehension of risks, benefits and alternatives to cardiac surgery, as well as decisional quality, and did not result in increased levels of anxiety.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".