Improving quality of withdrawal of life-sustaining measures in organ donation: a framework and implementation toolkit
Bibliographic record
Abstract
BACKGROUND: Donation after circulatory determination of death (DCD) is responsible for the largest increase in deceased donation over the past decade. When the Canadian DCD guideline was published in 2006, it included recommendations to create standard policies and procedures for withdrawal of life-sustaining measures (WLSM) as well as quality assurance frameworks for this practice. In 2016, the Canadian Critical Care Society produced a guideline for WLSM that requires modifications to facilitate implementation when DCD is part of the end-of-life care plan. METHODS: A pan-Canadian multidisciplinary collaborative was convened to examine the existing guideline framework and to create tools to put the existing guideline into practice in centres that practice DCD. RESULTS: A set of guiding principles for implementation of the guideline in DCD practice were produced using an iterative, consensus-based approach followed by development of four implementation tools and three quality assurance and audit tools. CONCLUSIONS: The tools developed will aid DCD centres in fulsomely adapting the Canadian Critical Care Society Withdrawal of Life-Sustaining Measures guideline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.225 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| 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".