Intraoperative radiation therapy with 50 kV x‐rays: A multi‐institutional review
Bibliographic record
Abstract
This report covers clinical implementation of a low kV intraoperative radiation therapy (IORT) program with the INTRABEAM® System (Carl Zeiss Meditec AG, Jena, Germany). Based on collective user experience from eight institutions, we discuss best methods of INTRABEAM quality assurance (QA) tests, commissioning measurements, clinical workflow, treatment planning, and potential avenues for research. The guide provides pertinent background information and clinical justification for IORT. It describes the INTRABEAM system and commissioning measurements along with a TG100 risk management analysis to ensure safety and accuracy of the IORT program. Following safety checks, dosimetry measurements are performed for verification of field flatness and symmetry, x-ray output, and depth dose. Also discussed are dose linearity checks, beam isotropy, ion chamber measurements, calibration protocols, and in-vivo dosimetry with optically stimulated luminescence dosimeters OSLDs, and radiochromic film. Emphasis is placed on the importance of routine QA procedures (daily, monthly, and annual) performed at regular intervals for a successful IORT program. For safe and accurate dose delivery, tests of important components of IORT clinical workflow are emphasized, such as, dose prescription, pre-treatment QA, treatment setup, safety checks, radiation surveys, and independent checks of delivered dose. Challenges associated with in-vivo dose measurements are discussed, along with special treatment procedures and shielding requirements. The importance of treatment planning in IORT is reviewed with reference to a Monte Carlo-based commercial treatment planning system highlighting its main features and limitations. The report concludes with suggested topics for research including CT-based image-guided treatment planning and improved prescription dose accuracy. We hope that this multi-institutional report will serve as a guidance document on the clinical implementation and use of INTRABEAM IORT.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".