The relative biological effectiveness of out-of-field dose
Bibliographic record
Abstract
PURPOSE: using simulations and models derived from existing literature, this work investigates relative biological effectiveness (RBE) for out-of-field radiation and attempts to quantify the relative magnitudes of different contributing phenomena (spectral, bystander, and low dose hypersensitivity effects). Specific attention is paid to external beam radiotherapy treatments for prostate cancer. MATERIALS AND METHODS: using different biological models that account for spectral, bystander, and low dose hypersensitivity effects, the RBE was calculated for different points moving radially out from isocentre for a typical single arc VMAT prostate case. The RBE was found by taking the ratio of the equivalent dose with the physical dose. Equivalent doses were calculated by determining what physical dose would be necessary to produce the same overall biological effect as that predicted using the different biological models. RESULTS: spectral effects changed the RBE out-of-field less than 2%, whereas response models incorporating low dose hypersensitivity and bystander effects resulted in a much more profound change of the RBE for out-of-field doses. The bystander effect had the largest RBE for points located just outside the edge of the primary radiation beam in the cranial caudal (z-direction) compared to low dose hypersensitivity and spectral effects. In the coplanar direction, bystander effect played the largest role in enhancing the RBE for points up to 8.75 cm from isocentre. CONCLUSIONS: spectral, bystander, and low dose hypersensitivity effects can all increase the RBE for out-of-field radiation doses. In most cases, bystander effects seem to play the largest role followed by low dose hypersensitivity. Spectral effects were unlikely to be of any clinical significance. Bystander, low dose hypersensitivity, and spectral effect increased the RBE much more in the cranial caudal direction (z-direction) compared with the coplanar directions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".