SP-0011 Unified Radiogenomic Prediction of Late Radiotherapy Toxicities
Bibliographic record
Abstract
The radiotherapy process is a series of events during which discrepancies between the planned treatment and actual treatment delivered can occur.This necessitates a comprehensive quality assurance (QA) programme, including regular quality control (QC) checks and audits.As more advanced technology is introduced in the clinical setting, QA activities must continually evolve to provide a safe framework for implementation of technical radiotherapy.With image guided and adaptive strategies being increasingly employed to ensure accurate delivery of treatment in scenarios such as dose escalation and hypofractionation; techniques must be implemented in a safe and effective manner.QA in the clinical trial arena has played a leading role in striving for accuracy and consistency of radiotherapy treatment delivery through monitoring protocol compliance in a multi-centre setting.Clinical trials can also evaluate the feasibility and effectiveness of a new technology.A comprehensive trial QA programme not only accredits centres for recruitment to a trial but also benefits the general standard of radiotherapy delivered.This presentation will aim to demonstrate how we can extend the clinical trial QA experience to routine practice to ensure quality of image guidance through discussing examples of clinical trial benchmarking and credentialing processes and their perceived impacts on clinical practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".