Bibliographic record
Abstract
Human tissues exhibit a varying response to radiation dose depending on the dose rate and fractionation scheme used. Dose rate effects have been reported for different radiations, and tissue types. The literature indicates that there is not a significant difference in response for low-LET radiation when using dose rates between 1 Gy min(-1) and 12 Gy min(-1) but lower dose rates have an observable sparing effect on tissues and a differential effect between tissues. In intensity-modulated radiotherapy such as volumetric modulated arc therapy (VMAT) the dose can be delivered with a wide range of dose rates. In this work we developed a method based on time-resolved Monte Carlo simulations to quantify the dose rate frequency distribution for clinical VMAT treatments for three cancer sites, head and neck, lung, and pelvis within both planning target volumes (PTV) and normal tissues. The results show a wide range of dose rates are used to deliver dose in VMAT and up to 75% of the PTV can have its dose delivered with dose rates <1 Gy min(-1). Pelvic plans on average have a lower mean dose rate within the PTV than lung or head and neck plans but a comparable mean dose rate within the organs at risk. Two VMAT plans that fulfil the same dose objectives and constraints may be delivered with different dose rate distributions, particularly when comparing single arcs to multiple arc plans. It is concluded that for dynamic plans, the dose rate range used varies to a larger degree than previously assumed. The effect of the dose rate range in VMAT on clinical outcome is unknown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".