What Should We Say When Discussing “Code Status” and Life Support with a Patient? A Delphi Analysis
Bibliographic record
Abstract
BACKGROUND: Patients and clinicians often find it difficult to discuss wishes regarding cardiopulmonary resuscitation (CPR) or "code status." Some authors have published effective communication styles, but there are currently no published guidelines for the content of a discussion about resuscitation or goals of care. METHODS: We identified a group of physicians with expertise in end-of-life care and communication, and used the Delphi method to develop a series of consensus statements about the ideal content of a discussion of CPR and goals of care. RESULTS: Twelve physicians agreed to participate in the study, generating nine consensus statements. These statements addressed the following topics: timing the discussion; framing the discussion in terms of "goals of care"; distinguishing between life-sustaining therapy (LST) and CPR; describing a cardiac arrest, LST, CPR, and palliative care; describing what happens after a cardiac arrest; how to modify the discussion to respect a patient's medical condition or beliefs; offering a prognosis; making a recommendation; and the importance of trust and rapport. There was consensus for each statement after the second Delphi round. INTERPRETATION: Physicians with expertise in end-of-life care and communication were able to develop consensus statements for the ideal content of a discussion of CPR and goals of care. These statements can serve as guidelines for physicians who feel uncomfortable with these discussions, in order to facilitate effective, informed, and ethically sound decision making.
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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 | 0.095 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".