Estimating and reducing dose received by cardiac devices for patients undergoing radiotherapy
Bibliographic record
Abstract
The objectives of this project are to quantify the dose reduction effect provided by a lead shield for patients with cardiac implantable electronic devices (CIED) during a clinically realistic radiation treatment on phantom and to provide a simple model of dose estimation to predict dose received by CIED in a wide range of situations. The shield used in this project is composed of a lead sheet wrapped in thermoplastic. Dose measurements were made with a plastic scintillation detector (PSD). The phantom was treated with ten different plans. Three of these cases were treated with intensity-modulated radiation therapy (IMRT) and the others received standard 3D conformal radiation therapy (3D CRT). Lateral dose measurement for photon fields was made to establish a dose prediction model. On average, the use of the lead shield reduced the dose to CIEDs by 19% ± 13%. Dose reduction was most important for breast cases, with a mean reduction of 31% ± 15%. In three cases, the total dose reduction was more than 25 cGy over the complete treatment. For the three IMRT cases, the mean dose reduction was 11% ± 9%. On average, the difference between the TPS prediction and the measurement was 71%, while it was only 14% for the dose prediction model. It was demonstrated that a lead shield can be efficiently used for reducing doses to CIED with a wide range of clinical plans. In patients treated with IMRT modality treatment, the shielding should be used only for those with more than two anterior fields over seven fields. In the case of 3D CRT patients, the shielding should be used for those with a dose on the CIED higher than 50 cGy and with a reduction of dose higher than 10 cGy. The dose prediction model developed in this study can be an easy way to have a better estimation of the out-of-field dose than the TPS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".