Bibliographic record
Abstract
Imaging is central to radiation oncology practice, with advances in radiation oncology occurring in parallel to advances in imaging. Targets to be irradiated and normal tissues to be spared are delineated on computed tomography (CT) scans in the planning process. Computer-assisted design of the radiation dose distribution ensures that the objectives for target coverage and avoidance of healthy tissue are achieved. The radiation treatment units are now recognized as state-of-the-art robotics capable of three-dimensional soft tissue imaging immediately before, during, or after radiation delivery, improving the localization of the target at the time of radiation delivery, to ensure that radiation therapy is delivered as planned. Frequent imaging in the treatment room during a course of radiation therapy, with decisions made on the basis of imaging, is referred to as image-guided radiation therapy (IGRT). IGRT allows changes in tumor position, size, and shape to be measured during the course of therapy, with adjustments made to maximize the geometric accuracy and precision of radiation delivery, reducing the volume of healthy tissue irradiated and permitting dose escalation to the tumor. These geometric advantages increase the chance of tumor control, reduce the risk of toxicity after radiotherapy, and facilitate the development of shorter radiotherapy schedules. By reducing the variability in delivered doses across a population of patients, IGRT should also improve interpretation of future clinical trials.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".