Interventions for Shared Decision‐Making About Life Support in the Intensive Care Unit: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Healthcare professionals and families make decisions about the use of life support for patients in the intensive care unit (ICU), including decisions to withhold or withdraw life support at the end-of-life. Best practice guidelines recommend using a shared decision-making (SDM) approach to improve the quality of end-of-life decision-making but do not describe how this should be done in practice. AIMS: To know what elements of SDM had been tested to improve communication between healthcare professionals, patients, and their family about the decision. Trials relevant to our review assessed whether these interventions were more effective than usual care. METHODS: A systematic review of randomized controlled trials of SDM interventions for the decision about using life support, limiting the use of life support, or withdrawing life support for hospitalized patients. We searched databases from inception to January 2011. RESULTS: Of 3,162 publications, four unique trials were conducted between 1992 and 2005. Of four trials, three interventions were evaluated. Two studies of interventions including three of nine elements of SDM did not report improvements in communication. Two studies of the same ethics consultation, which included eight of nine elements of SDM, did not evaluate the benefit to communication. The interventions were not harmful; they decreased family member anxiety and distress, shortened intensive care unit stay, but did not affect patient mortality. IMPLICATIONS FOR RESEARCH AND PRACTICE: Few studies have evaluated interventions to improve communication between healthcare professionals and patients/families when facing the decision about whether or not to use life support in the ICU. Interventions that include essential elements of SDM need to be more thoroughly evaluated in order to determine their effectiveness and health impact and to guide clinical practice.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".