Sci—Fri PM: Delivery — 11: Accuracy considerations in modern radiation oncology: An update
Bibliographic record
Abstract
The most recent reviews of accuracy requirements in radiation oncology were published in the 1990s, primarily in an era that was transitioning from 2-D to 3-D conformal radiation therapy (CRT). Since then, the technology associated with radiation oncology has changed dramatically. The combination of various forms of imaging for radiation therapy planning, treatment planning software, dose delivery technology including 4-D considerations as well as in-room daily image guidance has resulted in new perspectives on accuracy considerations. The underlying hypothesis for the use of these advanced technologies is that loco-regional control of cancer remains a significant barrier to cancer cure for many common cancers and that better dose distributions will translate into better outcomes. However, further clinical gain using these new technologies may be limited by single or compounded uncertainties associated with the entire treatment process. Thus, it is important to understand what factors should be considered in determining accuracy requirements as well as the realistic expectations of uncertainties that exist within the total treatment process. The need for accuracy is based on clinical requirements such as the steepness of dose-response curves, inherent heterogeneity in patient response to treatment, and the level of accuracy that is practically achievable. Statements on accuracy are dependent on the technology used and the reality of what is practically achievable and necessary. This review highlights some of the major differences between accuracy requirements as determined in the 2-D RT and 3-D CRT era versus the modern era of intensity modulated, image-guided, 4-D radiation therapy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".