Perioperative do-not-resuscitate orders--doing 'nothing' when 'something' can be done.
Bibliographic record
Abstract
Cardiopulmonary resuscitation (CPR) has the ability to reverse premature death. It can also prolong terminal illness, increase discomfort and consume enormous resources. Despite the desire to respect patient autonomy, there are many reasons why withholding CPR may be complicated in the perioperative setting. This review outlines these factors in order to offer practical suggestions and to provoke discussion among perioperative care providers. Although originally described for witnessed intraoperative arrests, closed chest cardiac massage quickly became universal practice, and a legal imperative in many hospitals. Concerns were raised by both health care workers and patient groups; this eventually led to the creation of the do-not-resuscitate (DNR) order. However, legal precedents and ethical interpretations dictated that patients were expected to receive full resuscitation unless there was explicit documentation to the contrary. In short, CPR became the only medical intervention that required an order to prevent it from being performed. Before the 1990s, patients routinely had pre-existing DNR orders suspended during the perioperative period. Several articles criticized this widespread practice, and the policy of 'required reconsideration' was proposed. Despite this, many practical issues have hindered widespread observance of DNR orders for surgical patients, including concerns related to the DNR order itself and difficulties related to the nature of the operating room environment. This review outlines the origins of the DNR order, and how it currently affects the patient presenting for surgery with a pre-existing DNR order. There are many obstacles yet to overcome, but several practical strategies exist to aid health care workers and patients alike.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".