Impact of advance directives on the variability between intensivists in the decisions to forgo life-sustaining treatment
Bibliographic record
Abstract
BACKGROUND: There is wide variability between intensivists in the decisions to forgo life-sustaining treatment (DFLST). Advance directives (ADs) allow patients to communicate their end-of-life wishes to physicians. We assessed whether ADs reduced variability in DFLSTs between intensivists. METHODS: We conducted a multicenter, prospective, simulation study. Eight patients expressed their wishes in ADs after being informed about DFLSTs by an intensivist-investigator. The participating intensivists answered ten questions about the DFLSTs of each patient in two scenarios, referring to patients' characteristics without ADs (round 1) and then with (round 2). DFLST score ranged from 0 (no-DFLST) to 10 (DFLST for all questions). The main outcome was variability in DFLSTs between intensivists, expressed as relative standard deviation (RSD). RESULTS: A total of 19,680 decisions made by 123 intensivists from 27 ICUs were analyzed. The DFLST score was higher with ADs than without (6.02 95% CI [5.85; 6.19] vs 4.92 95% CI [4.75; 5.10], p < 0.001). High inter-intensivist variability did not change with ADs (RSD: 0.56 (round 1) vs 0.46 (round 2), p = 0.84). Inter-intensivist agreement on DFLSTs was weak with ADs (intra-class correlation coefficient: 0.28). No factor associated with DFLSTs was identified. A qualitative analysis of ADs showed focus on end-of-life wills, unwanted things and fear of pain. CONCLUSIONS: ADs increased the DFLST rate but did not reduce variability between the intensivists. In the decision-making process using ADs, the intensivist's decision took priority. Further research is needed to improve the matching of the physicians' decision with the patient's wishes. Trial registration ClinicalTrials.gov Identifier: NCT03013530. Registered 6 January 2017; https://clinicaltrials.gov/ct2/show/NCT03013530 .
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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 | 0.041 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".