The Role of Big Data in Radiation Oncology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
More than half of cancer patients receive ionizing radiation as part of their treatment and it is the main modality at advanced stages of disease. Treatment outcomes in radiotherapy are determined by complex interactions between cancer genetics, treatment regimens, and patient-related variables. A typical radiotherapy treatment scenario can generate a large pool of data, “Big data,” that is comprised of patient demographics, dosimetry, imaging features, and biological markers. Radiotherapy data constitutes a unique interface between physical and biological data interactions. In this chapter, the authors review recent advances and discuss current challenges to interrogate big data in radiotherapy using top-bottom and bottom-top approaches. They describe the specific nature of big data in radiotherapy and discuss issues related to bioinformatics tools for data aggregation, sharing, and confidentiality. The authors also highlight the potential opportunities in this field for big data research from bioinformaticians as well as clinical decision-makers' perspectives.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it