[Improvement of dose distributions at NIRS's 70 MeV proton eye treatment beam course]
Bibliographic record
Abstract
In order to improve dose distributions at NIRS's 70 MeV proton eye treatment beam course, we introduced finer bar ridge filters, and examined the effects of range compensators. The pitch of new bar ridge filters was 5mm in contrast to 15mm pitch of old ones. A NC-machine recently available enabled this refinement. The spread out Bragg peak (SOBP) widths were 10, 15, 20 and 30 mm. The new ridge filters improved the field uniformity considerably. In the ridge filter design we assumed parallel beam condition in which the mono-energetic proton should proceed in parallel with the central axis, and bar ridges only changes the proton ranges. We searched empirically for the optimum wobbler radius in view of field flatness and depth dose distribution. Range shifter and compensator did not affect the field flatness and depth dose distribution at the optimum condition thus searched. We measured dose distribution in a phantom using a compensator of stairs-shape, which fairly modulated the beam. A 50% isodose line almost coincided with the compensator shape, and these results suggested that improvements of dose distributions should be possible using compensators. However width between 50% and 80% isodose lines depended on the thickness of phantom. This might be due to scattering in the compensator and suggests that it is necessary to calculate dose distribution taking account of such effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".