Exploration of Withdrawal of Life-Sustaining Therapy in Canadian Intensive Care Units
Bibliographic record
Abstract
OBJECTIVE: The process of controlled donation after circulatory death (cDCD) is strongly connected with the process of withdrawal of life-sustaining therapy. In addition to impacting cDCD success, actions comprising withdrawal of life-sustaining therapy have implications for quality of palliative care. We examined pilot study data from Canadian intensive care units to explore current practices of life-sustaining therapy withdrawal in nondonor patients and described variability in standard practice. DESIGN: Secondary analysis of observational data collected for Determination of Death Practices in Intensive Care pilot study. SETTING: Four Canadian adult intensive care units. PATIENTS: Patients ≥18 years in whom a decision to withdraw life-sustaining therapy was made and substitute decision makers consented to study participation. Organ donors were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Prospective observational data on interventions withdrawn, drugs administered, and timing of life-sustaining therapy withdrawal was available for 36 patients who participated in the pilot study. Of the patients, 42% died in ≤1 hour; median length of time to death varied between intensive care units (39-390 minutes). Withdrawal of life-sustaining therapy processes appeared to follow a general pattern of vasoactive drug withdrawal followed by withdrawal of mechanical ventilation and extubation in most sites but specific steps varied. Approaches to extubation and weaning of vasoactive drugs were not consistent. Protocols detailing the process of life-sustaining therapy withdrawal were available for 3 of 4 sites and also exhibited differences across sites. CONCLUSIONS: Standard practice of life-sustaining therapy withdrawal appears to differ between selected Canadian sites. Variability in withdrawal of life-sustaining therapy may have a potential impact both on rates of cDCD success and quality of palliative 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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".