Variations in the Operational Process of Withdrawal of Life-Sustaining Therapy
Bibliographic record
Abstract
OBJECTIVE: The process of withdrawal of life-sustaining therapy remains poorly described in the current literature despite its importance for patient comfort and optimal end-of-life care. We conducted a structured review of the published literature to summarize patterns of withdrawal of life-sustaining therapy processes in adult ICUs. DATA SOURCES: Electronic journal databases were searched from date of first issue until April 2014. STUDY SELECTION: Original research articles describing processes of life-support therapy withdrawal in North American, European, and Australian ICUs were included. DATA EXTRACTION: From each article, we extracted definitions of withdrawal of life-sustaining therapy, descriptions and order of interventions withdrawn, drugs administered, and timing from withdrawal of life-sustaining therapy until death. DATA SYNTHESIS: Fifteen articles met inclusion criteria. Definitions of withdrawal of life-sustaining therapy varied and focused on withdrawal of mechanical ventilation; two studies did not present operational definitions. All studies described different aspects of process of life-support therapy withdrawal and measured different time periods prior to death. Staggered patterns of withdrawal of life-support therapy were reported in all studies describing order of interventions withdrawn, with vasoactive drugs withdrawn first followed by gradual withdrawal of mechanical ventilation. Processes of withdrawal of life-sustaining therapy did not seem to influence time to death. CONCLUSIONS: Further description of the operational processes of life-sustaining therapy withdrawal in a more structured manner with standardized definitions and regular inclusion of measures of patient comfort and family satisfaction with care is needed to identify which patterns and processes are associated with greatest perceived patient comfort and family satisfaction with care.
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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 | 0.067 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".