Discussions on implantable cardioverter defibrillator deactivation in patients receiving radiation therapy
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): University of Toronto Background Established guidelines discuss ICD deactivation and end of life care in ICD patients. A previous study has suggested that 45% of patients with an ICD and do not resuscitate order have not discussed ICD deactivation. In patients receiving radiation therapy for cancer ICD deactivation should be discussed given the life limiting nature of cancer, however it is unclear how frequently this topic is discussed in clinical practice. Objectives To report on the frequency of discussions of ICD deactivation in a population of patients receiving radiation therapy for cancer. Methods Our hospital is a large regional cancer and cardiac center in Canada. Consecutive patients of our institution presenting to our ICD clinic for ICD checks prior and during receipt of radiation therapy between 2005 and 2019 were included (n=43). Electronic medical records were reviewed to determine demographics, clinical characteristics, documentation of discussions on ICD deactivation, and survival at 1-year. Results The cohort was predominantly male (84%), with a primary prevention ICD (65%) without resynchronization capacity (74%). Discussions on ICD deactivation with subsequent deactivations occurred in 12 (28%) patients. During a median follow-up of 4 years 25 patients died, with 28% of these patients dying within 2 years of the ICD clinic encounter. Discussions on ICD deactivation were more frequent in patients who received a palliative care consultation. Conclusions End of life conversations regarding ICD deactivation in patients undergoing radiation therapy for cancer are rare. Collaboration with palliative care teams may facilitate conversations on ICD deactivation during this opportune time.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".