Poster - Thur Eve - 11: A realistic respiratory trace generator and its application to respiratory management techniques
Bibliographic record
Abstract
Respiratory motion complicates radiotherapy treatment of thoracic and abdominal tumours. Simplified respiratory motions such as sinusoidal and single patient traces are often used to determine the impact of motion on respiratory management techniques in radiotherapy. Such simplifications only accurately model a small portion of patients, as most patients exhibit variability and irregularity beyond these models. We have preformed a comprehensive analysis of respiratory motion and developed a software tool that allows for explicit inclusion of variability. We utilize our realistic respiratory generator to customize respiratory traces to test the robustness of the estimate of internal gross target volumes (IGTV) by 4DCT and CBCT. We confirmed that good agreement is found between 4DCT and CBCT for regular breathing motion. When amplitude variability was introduced the accuracy of the estimate slightly, but the absolute differences were still < 3 mm for both modalities. Poor agreement was shown with the addition of baseline drifts. Both modalities were found to underestimate the IGTV by as much as 30% for 4DCT and 25% for CBCT. Both large and small drifts deteriorated the estimate accuracy. The respiratory trace generator was advantageous for examining the difference between 4DCT and CBCT IGTV estimation under variable motions. It provided useful implementation abilities to test specific attributes of respiratory motion and detected issues that were not seen with the regular motion studies. This is just one example of how the respiratory trace generator can be utilized to test applications of respiratory management techniques.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".