Intraoperative radiation therapy using mobile electron linear accelerators: Report of AAPM Radiation Therapy Committee Task Group No. 72
Bibliographic record
Abstract
Intraoperative radiation therapy (IORT) has been customarily performed either in a shielded operating suite located in the operating room (OR) or in a shielded treatment room located within the Department of Radiation Oncology. In both cases, this cancer treatment modality uses stationary linear accelerators. With the development of new technology, mobile linear accelerators have recently become available for IORT. Mobility offers flexibility in treatment location and is leading to a renewed interest in IORT. These mobile accelerator units, which can be transported any day of use to almost any location within a hospital setting, are assembled in a nondedicated environment and used to deliver IORT. Numerous aspects of the design of these new units differ from that of conventional linear accelerators. The scope of this Task Group (TG-72) will focus on items that particularly apply to mobile IORT electron systems. More specifically, the charges to this Task Group are to (i) identify the key differences between stationary and mobile electron linear accelerators used for IORT, (ii) describe and recommend the implementation of an IORT program within the OR environment, (iii) present and discuss radiation protection issues and consequences of working within a nondedicated radiotherapy environment, (iv) describe and recommend the acceptance and machine commissioning of items that are specific to mobile electron linear accelerators, and (v) design and recommend an efficient quality assurance program for mobile systems.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".