“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
Purpose: Our aim was to understand the decision making of patients in hospital who wanted cardiopulmonary resuscitation despite low probability of benefit. Methods: We 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. Results: We 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. Limitations: The probability of successful CPR in our sample is unknown. Findings may be different in a population who is imminently dying but still requesting CPR. Conclusions: Participants 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. Implications: Decision 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 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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".