Technical Note: Enhancing the surface dose using a weak longitudinal magnetic field
Bibliographic record
Abstract
PURPOSE: The surface dose in radiotherapy is subject to the physical properties of the radiation beam and collimator. The purpose of this work is to investigate the manipulation of surface dose using magnetic fields produced with a resistive magnet. Better understanding of the feasibility and mechanisms of altered surface dose could have important clinical applications where the surface dose must be increased for therapeutic goals, or reduced to enhance the therapeutic benefit. METHODS: A resistive magnet capable of generating a peak magnetic field up to 0.24 T was integrated with a cobalt treatment unit. The magnetic fringe field of the magnet was small due to the self-shielding built within the magnet. The magnetic field at the beam collimation jaws of the cobalt irradiator was less than 10 G. The surface dose and depth dose were measured for varying magnetic field strengths. RESULTS: The resistive magnet was able to alter the dose in the buildup region of the (60)Co depth dose significantly, and the magnitude of dose enhancement was directly related to the strength of the longitudinal magnetic field. Peak magnetic fields as low as 0.08 T were able to affect the surface dose. At a peak field of 0.24 T, the authors measured a surface dose enhancement of 2.8-fold. CONCLUSIONS: Surface dose enhancement using resistive magnets is feasible. Further experimental study is needed to understand the origin of the scattered electrons that contribute to the increase in surface dose.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".