Quality dying: An approach to ICD deactivation in the hospital setting
Bibliographic record
Abstract
BACKGROUND: In Canada, cardiovascular disease is the second most common cause of death. A subset of these patients will require a cardiovascular implantable electronic device (CIED). An estimated 200 000 Canadians are living with a CIED. CIEDs can improve life and prevent premature death. However, when patients reach the end of their lives, they can pose a challenge. An example of which is a painful shock delivered from an implantable cardioverter defibrillator (ICD) for an arrhythmia in a dying patient. Receiving a shock at the end of life (EOL) is unacceptable in an age when we aim to ease the suffering of the dying and allow for a comfortable death. METHODS: As a quality standard of practice, all clinicians are expected to engage in EOL conversations in patients requiring CIED deactivation. Due to the potential discomfort of an ICD shock, specific conversations about deactivation of an ICD are encouraged. A process improvement approach was developed by our hospital that included an advance care planning simulation lab, electronic documentation and a standardized comfort measures order set that includes addressing the need for ICD deactivation at EOL. RESULTS: EOL conversations are complex. Health care providers have been equally challenged to have conversations about ICD deactivation. Standardization of the process of ICD deactivation ensures an approach to EOL which respects the individuality of patients and promotes quality dying. CONCLUSION: Our hospital is committed to assisting clinicians to provide quality care by improving conversations about EOL care. On the basis of a synthesis of existing literature, we describe the importance of and the ideal process for having EOL conversations in patients about ICD deactivation at the EOL.
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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 | 0.064 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".