Withdrawing Versus not Offering Cardiopulmonary Resuscitation: Is There a Difference?
Bibliographic record
Abstract
Conflict between substitute decision makers (SDMs) and health care providers in the intensive care unit is commonly related to goals of treatment at the end of life. Based on recent court decisions, even medical consensus that ongoing treatment is not clinically indicated cannot justify withdrawal of mechanical ventilation without consent from the SDM. Cardiopulmonary resuscitation (CPR), similar to mechanical ventilation, is a life‐sustaining therapy that can result in disagreement between SDMs and clinicians. In contrast to mechanical ventilation, in cases for which CPR is judged by the medical team to not be clinically indicated, there is no explicit or case law in Canada that dictates that withholding/not offering of CPR requires the consent of SDMs. In such cases, physicians can ethically and legally not offer CPR, even against SDM or patient wishes. To ensure that nonclinically indicated CPR is not inappropriately performed, hospitals should consider developing ‘scope of treatment’ forms that make it clear that even if CPR is desired, the individual components of resuscitation to be offered, if any, will be dictated by the medical team’s clinical assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.091 | 0.295 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 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".