Development and Pilot-Testing of a Patient Decision Aid for Left Ventricular Assist Device Placement
Bibliographic record
Abstract
Background Studies indicate suboptimal patient understanding of the capabilities, lifestyle implications, and risks of LVAD therapy. This paper describes the development methodology and pilot-testing of a decision aid for Left Ventricular Assist Device (LVAD) placement, combining traditional needs-assessment with a novel user- centered approach. Methods and Results We developed the decision aid in line with the Ottawa Decision Support Framework (ODSF) and the International Patient Decision Aids Standards (IPDAS) for ensuring quality, patient-centered content. Structured interviews were conducted with patients, caregivers, candidates for LVAD treatment, and expert clinicians (n=71) to generate content based on patient values and decisional needs, and providers’ perspectives on knowledge needs for informed consent. The aid was alpha tested through cognitive interviews (n=5) and acceptability tested with LVAD patients (n=10), candidates (n=10), and clinicians (n=13). Patients, caregivers and clinicians reported they would recommend the aid to patients considering treatment options for heart failure. Patients and caregivers agreed that the decision aid is a balanced tool presenting risks and benefits of LVAD treatment and generating discussion about aspects of heart failure treatment that matter most to patients. Conclusion We identified gaps in knowledge about heart failure treatment options, including diagnosis, decision-making, surgery, post-operative maintenance and lifestyle changes. Challenges included presenting risks and benefits for informed decision making without frightening patients and circumventing reflection, and balancing an emphasis on LVAD with other alternative treatment options like comfort- directed palliative and supportive care.
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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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".