Abstract 276: A Qualitative Analysis of Shared Decision Making in Cardiac Surgery
Bibliographic record
Abstract
OBJECTIVES Comprehension of risks, benefits, and alternative treatment options is poor among patients referred for cardiac interventions. We have previously demonstrated that frail, elderly patients undergoing cardiac surgery require complex procedures and are at markedly increased risk of postoperative death and prolonged institutional care. An effective informed consent process is critical in this population. We suggest this vulnerable patient population may benefit from the institution of a formalized shared decision making (SDM) process. METHODS Three focus groups were convened for CABG, Valve, or CABG +Valve patients over 70 who were either within two years post-op, within 4-8 weeks post-op or had had a complicated post-operative course. Two focus groups were convened for the caretaker group: IMCU nurses & ICU nurses and surgeons, anesthesiologists & cardiac intensivists. In a semi-structured interview format, groups were asked questions regarding personal experience with informed consent, comprehension of discussions prior to surgery, potential improvements to the consent process, and SDM in cardiac surgery. Transcribed audio data was analyzed to develop consistent and comprehensive themes. RESULTS Patient groups were supportive of changing standard consent by including patient-specific risk factors through graphics, reduced language complexity and increased font size as means to improve comprehension and discussion. Patient groups felt access to this information earlier on in their care would allow time to identify personal values and desires for treatment. Both care provider groups supported a consent process that would provide patients with information earlier through decisional aids presented in a structured SDM process. All groups were supportive of a dedicated RN employed as a decisional coach to meet with patients and families prior to surgery to discuss their values, concerns, and questions to facilitate SDM with the care team. CONCLUSIONS Data from these groups will aid in the development of decision aids that serve to educate patients about their disease, the procedure proposed, and its risks and alternatives. Utilizing validated risk prediction models from our own experience allows us to provide patient specific risks for in-hospital mortality, major morbidity, and prolonged institutional care as well as long term outcomes freedom from mortality and re-hospitalization for cardiac cause.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".