Management of Patients with Cardiovascular Implantable Electronic Devices Undergoing Radiation Therapy: A National Survey of Canadian Multidisciplinary Radiation Oncology Professionals
Bibliographic record
Abstract
Purpose This study aimed to characterize contemporary management of Canadian radiotherapy (RT) patients with cardiovascular implantable electronic devices (CIED), in light of updated American Association of Physicists in Medicine (AAPM) guidelines. Methods and Materials A 22-question, web-based survey was distributed to members of the Canadian Association of Radiation Oncology, Canadian Organization of Medical Physicists, and Canadian Association of Medical Radiation Technologists from January to February 2020. Respondent demographics, knowledge, and management practices were elicited. Statistical comparisons by respondent demographics were performed using Chi-square and Fisher's exact tests. Results In total, 155 surveys were completed by 54 radiation oncology (RO), 26 medical physics (MP), and 75 radiation therapy (RTh) respondents in academic (51%) and community (49%) practices across all provinces. The majority of respondents (77%) had managed >10 patients with CIEDs in their career. Most respondents (70%) reported using risk-stratified institutional management protocols. Respondents used manufacturer recommendations, rather than AAPM or institutionally recommended dose limits, when the manufacturer limit is 0 Gy (44%), 0-2 Gy (45%), or >2 Gy (34%). The majority of respondents (86%) reported institutional policies to refer to a cardiologist for CIED evaluation both prior to and after completion of RT. Cumulative dose to CIED, pacing dependence, and neutron production were considered during risk stratification by 86%, 74%, and 50% of participants, respectively. Dose and energy thresholds for high-risk management were not known by 45% and 52% of respondents, with ROs and RThs significantly less likely to report thresholds than MPs (p<0.001). Although 59% of respondents felt comfortable managing patients with CIEDs, community respondents were less likely to feel comfortable than academic respondents (p=0.037). Conclusions The management of Canadian RT patients with CIEDs is characterized by variability and uncertainty. National consensus guidelines may have a role in improving provider knowledge and confidence in caring for this growing population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".