Poster — Thur Eve — 60: Development of an Empirical Model for Respiratory Motion
Bibliographic record
Abstract
In order to simulate the effects of respiratory chest motion on breast cancer radiotherapy treatments, an accurate model of clinically relevant breathing patterns is needed. Current models of the chest wall are simple and ignore population variation in breathing shape and the asymmetries in inhale and exhale phases of the breathing cycle. This study aims to develop a realistic, empirical breathing model of the external motion of the chest wall. Development of this model will allow examination of how different breathing patterns may affect treatment deliverability and patient outcomes. We fit multiple sigmoidal functions to clinical respiratory data acquired by Varian's Real‐Time Position Management System (RPM, Varian Medical Systems, California). Nonlinear regression was used to quantify the goodness of fit parameters for the empirical model. Increasing the number of variables in the model increases the goodness of fit, and we used Akaike's Information Criterion (AIC) to make direct comparison of models accounting for the difference in the number of parameters. Using AIC, we found that for the inhale portion, a sigmoidal fit with four parameters was the most appropriate, while the exhale portion fit with a sigmoidal curve the three could be used instead of the four parameter fit. AIC information theory proved to be a useful tool in determining the likelihood of fit while accounting for the number of parameters. Further investigation will attempt to reduce the number of parameters by investigating trends in the coefficients of the fit.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 0.001 |
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".