Decision tools for life support: A review and policy analysis
Bibliographic record
Abstract
OBJECTIVE: To identify, describe, and compare published documents intended to guide decisions about the administration, withholding, or withdrawal of life support in critical care. DESIGN: Review article. SETTING AND SOURCES: Publicly available, English-language guidelines or decision tools for life support, identified through systematic literature search. MEASUREMENTS AND MAIN RESULTS: Forty-nine documents were included and coded for authorship, source, development methodology, format, and positions taken on 12 common life-support issues. Sources were independent academics (n=21, 43%), professional organizations (n=19, 44%), and provider organizations. Eighteen documents (37%) described no development method. Twenty-three (47%) were produced collectively (e.g., by committees or consensus conference), 7 (14%) mentioned a literature review, and 2 (4%) were based upon the author's professional experience. Tools differed in format and focus; we characterize three types as decision schemas (involving clinical practice algorithms; n=7, 14%), decision guides (reviewing legal or professional positions; n=29, 59%), and decision counsels (more discursive and focusing typically on ethical issues; n=13, 27%). Tools addressed 12 common life-support issues: advance directives (67%), resource considerations (51%), ICU discharge criteria (27%), ICU admission criteria (16%), whether withholding differs from withdrawing life support (59%), whether nutrition and hydration decisions are different from decisions about other types of life support (61%), euthanasia (49%), double effect (47%), brain death (35%), special considerations for patients in a persistent vegetative state (51%), potential organ donors (12%), and pregnant patients (10%). Positions on these key life-support issues varied. CONCLUSIONS: Published tools for guiding life-support decisions vary widely in their genesis, authorship, format, focus, and practicality. They also differ in their attention to, and positions on, key life-support dilemmas. Future research on decision tools should focus on how users interpret and apply the messages in these tools and their impacts on practice, quality of care, participant experiences, and outcomes.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".