Bibliographic record
Abstract
Each year four million adults in North America require a surrogate to make decisions for them after being admitted to an intensive care unit (ICU). These decisions frequently involve the limitation of life-sustaining treatments. The current paradigm for making these decisions requires surrogates to rely first on any advance directives from the patient, then on the surrogate’s substituted judgment, and finally on best interests as judged by a reasonable person. Since this paradigm emerged 40 years ago, hundreds of research studies have revealed conceptual and operational deficiencies with it and have documented the harms it may cause to patients, surrogates, and medical professionals. The accumulated weight of these studies motivates the central research question of my dissertation: What shifts to the current paradigm for surrogate decision-making might alleviate its clinical and ethical deficiencies? I address this question as an interdisciplinary neuroethics scholar relying on the research methods of interpretive description and qualitative metasynthesis to organize the accumulated evidence into pragmatic recommendations. This work required three separate but linked studies. In Study #1 I mined research on surrogates’ experiences to identify factors that influence their decision-making. In Study #2 I synthesized research on surrogate-professional relationships to identify gaps and conflicts between the decision factors from Study #1 and surrogate-professional interactions. In Study #3 I analyzed all seven editions of Beauchamp and Childress’ Principles of Biomedical Ethics (1979 to 2013), charting the evolution of bioethical thought regarding incompetent patients, and linking these changes to the results of studies #1 and #2. The findings from these studies informed three changes I propose to the paradigm and practice of surrogate decision-making in ICU. My proposal integrates the decision standard of individual best interests, a standardized values portrait capturing the critical values underlying each patient’s individual best interests, and an interest-specific/time-limited decision protocol. Future work will be needed to test the validity and effectiveness of these changes individually and as an integrated solution. Ultimately, the changes I propose are designed to enhance the consistency, continuity, and coordination of care for decisionally incapacitated ICU patients and to yield substantial benefits to surrogate decision-makers and medical professionals.
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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 | 0.270 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.071 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".