Bibliographic record
Abstract
Flash radiotherapy (Flash-RT) is an innovative technique used in radiotherapy for cancer treatment because it delivers an extremely high dose of radiation (>40 Gy/s) to the tumour in a very short period of time, typically within a fraction of a second. This ultra-fast delivery of radiation distinguishes Flash-RT from conventional radiotherapy, which typically involves the delivery of radiation over a longer time period, often several minutes. Studies conducted in cell and preclinical models suggested that Flash-RT may spare normal tissues from radiation-related side effects, such as skin toxicity, gastrointestinal complications, and damage to organs-at-risk. This is believed to be due to the unique normal tissue response to the ultra-high dose rate. Nevertheless, while Flash-RT shows promising results in preclinical and early clinical studies, one should note that the technique is still in the early stages of development. This entry provides a comprehensive exploration of the immense potentials of Flash-RT, covering its background, mechanisms, radiation sources, recent experimental findings based on cell and preclinical models, and future prospects. It aims to provide valuable insights into this innovative radiotherapy technology for anyone interested in the subject.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".