Awareness of do-not-resuscitate orders: what do patients know and want?
Bibliographic record
Abstract
OBJECTIVE: To assess outpatient understanding of and previous experiences with do-not-resuscitate (DNR) orders and to gauge patient preferences with respect to DNR discussions. DESIGN: Cross-sectional, self-administered survey. SETTING: Four urban primary care physician offices in Vancouver, BC. PARTICIPANTS: A total of 429 consecutive patients 40 years of age and older presenting for routine primary care between March and May 2009. MAIN OUTCOME MEASURES: Awareness of, knowledge about, and experiences with DNR decisions; when, where, and with whom patients wished to discuss DNR decisions; and differences in responses by sex, age, and ethnicity, assessed using χ² tests of independence. RESULTS: The response rate was 90%, with 386 of 429 patients completing the surveys. Most (84%) respondents had heard of the terms do not resuscitate or DNR. Eighty-six percent chose family physicians as among the people they most preferred to discuss DNR decisions with; 56% believed that initial DNR discussions should occur while they were healthy; and 46% thought the discussion should take place in the office setting. Of those who were previously aware of DNR orders, 70% had contemplated DNR for their own care, with those older than 60 years more likely to have done so (P = .02); however, only 8% of respondents who were aware of DNR orders had ever discussed the subject with a health care provider. Few patients (16%) found this topic stressful. CONCLUSION: Most respondents were well informed about the meaning of DNR, thought DNR discussions should take place when patients were still healthy, preferred to discuss DNR decisions with family physicians, and did not consider the topic stressful. Yet few respondents reported having had a conversation about DNR decisions with any health care provider. Disparity between patient preferences and experiences suggests that family physicians can and should initiate DNR discussions with younger and healthier patients.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".