Evaluation of a Novel Cardiopulmonary Resuscitation (CPR) Video Decision aid to Promote Shared Decision-making with Nephrology Patients and Families
Bibliographic record
Abstract
People with end stage renal disease (ESRD) face important health-related decisions concerning end-of-life care and the use of life-support technologies. They already require technology to sustain life, relying on dialysis to provide kidney function. People with ESRD have a high risk of cardiac arrest because dialysis worsens cardiac disease. In the case of cardiac arrest, the healthcare team may decide to offer cardiopulmonary resuscitation (CPR) to try to restore function and prolong life. While people often want to be involved in making decisions about their health, there are many challenges. People with advanced illness may have limited or wavering ability to participate fully in decision-making conversations – or lack decisional capacity for making decisions. Additionally, they may have a limited understanding of CPR and tend to receive inconsistent information on the process and outcome of CPR. CPR is less effective in older adults with advanced disease of any kind, resulting in difficult discussions between patients, their families, and healthcare professionals. Unfortunately, these discussions are often avoided. Shared decision-making approaches target overcoming these challenges. The objectives of this research are: 1) to analyze the concept of decisional capacity as it relates to medical decision-making and, 2) to design, test, and analyze a novel CPR video decision aid (VDA) with nephrology patients and their families in a clinical setting. The Interprofessional Shared Decision-making Model was used as a framework to guide the research. Results of from the study indicate that CPR-VDA was feasible and acceptable to patients with ESRD, their families, and the healthcare team in the nephrology setting, even when patients’ illness and treatment caused difficulty attending to all aspects of the decision-making process all of the time. The CPR-VDA improved patient and family knowledge about CPR, clarified values around the decision, improved the patients’ ability to make a decision about CPR confidently, and reduced decisional conflict (uncertainty) amongst patients, families, and physicians despite any limitations to patient decisional capacity. All patients in the study were able to participate in conversations and decision-making about CPR with the assistance of the CPR-VDA and decision coaching from an advanced practice nurse.
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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 | 0.006 | 0.023 |
| 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 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".