Re-analyzing Heart Failure Treatment Outcomes Based on Patient Preferences: A Study Using Data from Multiple Clinical Trials
Bibliographic record
Abstract
Heart failure (HF) is a serious condition affecting many adults worldwide, leading to poor quality of life, shorter life expectancy, and frequent hospitalizations. Patients with HF have multiple medication options, each with different benefits and risks. Deciding on the best treatment is complex for both patients and doctors, as it involves considering various factors, including survival, quality of life, and potential side effects. Current clinical trials often assess medications based on broad outcomes, like the time to a significant event (such as death or hospitalization), but these trials can be difficult to interpret for individual patients because the impact of treatments can vary greatly. To address this, a new method called the "win ratio" has been developed. The win ratio compares different treatments by looking at several important outcomes, such as survival, side effects, and quality of life, and ranks them based on what matters most. This method allows us to weigh each treatment’s benefits and drawbacks more clearly, based on what is important to patients. For example, if a patient values living a longer life but is okay with a few side effects, the win ratio helps us determine which treatment provides the best balance of benefits. However, no study has yet used patient preferences to decide which outcomes are the most important in the win ratio analysis. This is where our study comes in. We aim to re-analyze existing large clinical trials on HF treatments by incorporating patients' personal preferences into the win ratio. To do this, we will use a technique called discrete-choice experiments (DCEs). In a DCE, patients are asked to choose between different treatment options that vary based on key factors, such as how well the treatment helps the heart, how it affects their daily life, and whether there are side effects. For example, patients might be asked if they would prefer a treatment with a high chance of survival but some side effects, or a treatment with fewer side effects but less effective in improving health. This helps us understand what is most important to each patient in their treatment decision. By applying these preferences to the win ratio, we can better understand how different treatments perform according to what patients truly value. In this way, we hope to make treatment decisions more personal and patient-centered, ensuring that treatments are better suited to the diverse needs and preferences of individuals with heart failure.
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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 | 0.269 | 0.457 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.037 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".