Patient handling system for carbon ion beam scanning therapy
Bibliographic record
Abstract
Our institution established a new treatment facility for carbon ion beam scanning therapy in 2010. The major advantages of scanning beam treatment compared to the passive beam treatment are the following: high dose conformation with less excessive dose to the normal tissues, no bolus compensator and patient collimator/multi-leaf collimator, better dose efficiency by reducing the number of scatters. The new facility was designed to solve several problems encountered in the existing facility, at which several thousand patients were treated over more than 15 years. Here, we introduce the patient handling system in the new treatment facility. The new facility incorporates three main systems, a scanning irradiation system (S-IR), treatment planning system (TPS), and patient handling system (PTH). The PTH covers a wide range of functions including imaging, geometrical/position accuracy including motion management (immobilization, robotic arm treatment bed), layout of the treatment room, treatment workflow, software, and others. The first clinical trials without respiratory gating have been successfully started. The PTH allows a reduction in patient stay in the treatment room to as few as 7 min. The PTH plays an important role in carbon ion beam scanning therapy at the new institution, particularly in the management of patient handling, application of image-guided therapy, and improvement of treatment workflow, and thereby allows substantially better treatment at minimum cost.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".