Malfunction of cardiac devices after radiotherapy without direct exposure to ionizing radiation: mechanisms and experimental data
Bibliographic record
Abstract
AIMS: Malfunctions of cardiac implantable electronical devices (CIED) have been described after high-energy radiation therapy even in the absence of direct exposure to ionizing radiation, due to diffusion of neutrons (n) causing soft errors in inner circuits. The purpose of the study was to analyse the effect of scattered radiation on different types and models of CIED and the possible sources of malfunctions. METHODS AND RESULTS: Fifty-nine explanted CIED were placed on an anthropomorphous phantom of tissue-equivalent material, and a high-energy photon (15 MV) radiotherapy course (total dose = 70 Gy) for prostate treatment was performed. All devices were interrogated before and after radiation. Radiation dose, the electromagnetic field, and neutron fluence at the CIED site were measured. Thirty-four pacemakers (PM) and 25 implantable cardioverter-defibrillators (ICD) were analysed. No malfunctions were detected before radiation. After radiation a software malfunction was evident in 13 (52%) ICD and 6 (18%) PM; no significant electromagnetic field or photon radiations were detected in the thoracic region. Neutron capture was demonstrated by the presence of the (198)Au((197)Au + n) or (192)Ir((191)Ir + n) isotope activation; it was significantly greater in ICD than in PM and non-significantly greater in damaged devices. A greater effect in St Jude PM (2/2 damaged), Boston (9/11), and St Jude ICD (3/6) and in older ICD models was observed; the year of production was not relevant in PM. CONCLUSION: High-energy radiation can cause different malfunctions on CIED, particularly ICD, even without direct exposure to ionizing radiation due to scattered radiation of neutrons produced by the linear accelerator.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".