Estimating the Impact of Words Used by Physicians in Advance Care Planning Discussions: The “Do You Want Everything Done?” Effect
Bibliographic record
Abstract
To estimate the probability of a substitute decision maker choosing to withdraw life-sustaining therapy after hearing an affirmative patient response to the phrase "Do you want everything done?" DESIGN: Discrete choice experiment. SETTING: Single community hospital in Ontario. SUBJECTS: Nonrandom sampling of healthcare providers and the public. INTERVENTION: Online survey. MEASUREMENTS AND MAIN RESULTS: Of the 1,621 subjects who entered the survey, 692 consented and 432 completed the survey. Females comprised 73% of subjects. Over 95% of subjects were under 65 years old, and 50% had some intensive care-related exposure. Healthcare providers comprised 29% of the subjects. The relative importance of attributes for determining the probability of withdraw life-sustaining therapy by substitute decision makers was as follows: stated patient preferences equals to 23.4%; patient age equals to 20.6%; physical function prognosis equals to 15.2%; length of ICU stay equals to 14.4%; survival prognosis equals to 13.8%; and prognosis for communication equals to 12.6%. Using attribute level utilities, the probability of an substitute decision maker choosing to withdraw life-sustaining therapy after hearing a patient answer in the affirmative "Do you want everything done?" compared with "I would not want to live if I could not take care of myself" was 18.8% (95% CI, 17.2-20.4%) versus 59.8% (95% CI, 57.6-62.0%) after controlling for all the other five attribute levels in the scenario: age greater than 80 years; survival prognosis less than 1%; length of ICU stay greater than 6 months; communication equals to unresponsive; and physical equals to bed bound. CONCLUSIONS: Using a discrete choice experiment survey, we estimated the impact of a commonly employed and poorly understood phrase physicians may use when discussing advance care plans with patients and their substitute decision makers on the subsequent withdraw life-sustaining therapies. This phrase is predicted to dramatically reduce the likelihood of withdraw life-sustaining therapy even in medically nonbeneficial scenarios and potentially contribute to low-value end-of-life care and outcomes. The immediate cessation of this term should be reinforced in medical training for all healthcare providers who participate in advance care planning.
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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 | 0.053 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".