Deciding about Heart Transplantation or Mechanical Support: An Empirical Study and Ethical Analysis
Bibliographic record
Abstract
Purpose: Patients living with advanced heart failure experience dyspnea, fatigue, poor quality of life, depression and cognitive impairment which may threaten their ability to provide informed consent to undergo heart transplant (HTx) or mechanical support (LVAD). Using qualitative and quantitative methods, we asked how patients with advanced heart failure make decisions regarding HTx and LVAD. The variables chosen to reflect the elements of consent included quality of life and symptom severity (voluntariness), depression and cognitive impairment (capacity) and treatment preferences (decision-making).\nMethods: 76 patients enrolled in the quantitative arm completed the Minnesota Living with Heart Failure Questionnaire; Visual Analog scales for dyspnea, fatigue and overall health; Beck Depression Inventory; Montreal Cognitive Assessment; Standard Gamble and Time Tradeoff. Qualitative methods were used to discover concepts, relationships and decision-making processes described by 17 of the 76 patients considering HTx and LVAD. \nResults: Patients reported poor quality of life and high symptom severity scores which compelled them to consider surgery as a way to relieve unpleasant symptoms and improve quality of life. Although 30% of patients had evidence of depression and/or cognitive impairment, no patient was deemed incapable of decision-making. Patients were willing to take considerable risk (35%) and trade considerable time (4months) to improve their health. While heart failure-related concepts were important to the decision, entrustment emerged as the meaningful process for decision-making.\nConclusions: Patients who participated in this study were capable of decision-making and understood the risks associated with the surgery. Voluntariness was diminished by disease but not absent, and decisions were free of coercion. These results suggest the entrustment model of decision-making is the dominant process for patients considering high-risk surgical procedures and meets criteria for informed consent. Understanding the process of decision-making will help clinicians support and enable treatment decisions made by patients living with advanced heart failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.111 | 0.227 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".