“To Be or Not to Be”—Cardiopulmonary Resuscitation for Hospitalized People Who Have a Low Probability of Benefit: Qualitative Analysis of Semi-structured Interviews
Bibliographic record
Abstract
PurposeOur aim was to understand the decision making of patients in hospital who wanted cardiopulmonary resuscitation despite low probability of benefit.MethodsWe included patients admitted to general medical wards who had a low chance of surviving in-hospital cardiopulmonary resuscitation (CPR) and had an order in the chart to administer CPR. We developed an interview guide to explore participants’ decision-making process, sources of information, and emotions associated with this decision.ResultsWe developed 3 themes from the data. 1) “Life is worth living . . . for now”: Participants describe their enjoyment of life and desire to carry on in their current state. 2) “Making sense of CPR outcomes”: Participants saw CPR outcomes as binary, either they live, or they die; deciding not to receive CPR means choosing death. Participants were optimistic they would survive CPR and cited personal experience and TV as information sources. 3) “Decision process”: Participants did not engage in shared decision making. Instead, they were asked a binary yes/no question with no reflection on their values or discussion about harms or benefits.LimitationsThe probability of successful CPR in our sample is unknown. Findings may be different in a population who is imminently dying but still requesting CPR.ConclusionsParticipants chose CPR because they perceived life as worth living and CPR as a chance worth taking. Participants did not want to be left in a severely debilitated state but did not have accurate information about this risk.ImplicationsDecision making about CPR in-hospital can be improved if it is grounded in accurate risk understanding and the patient’s values and wishes.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".