Poster — Thur Eve — 13: Monitoring the Breathing Patterns of Lung Patients throughout the Course of Treatment — Preliminary Experience with the RADPOS System
Bibliographic record
Abstract
The RADPOS system is a new in vivo dosimetry tool that combines a MOSFET dosimeter with an electromagnetic positioning sensor to allow for simultaneous measurement in real‐time of dose and spatial coordinates at a specific location. A study is currently underway using the RADPOS system during the 4DCT and external beam treatments of lung cancer patients. Each day, RADPOS detectors are positioned at marked points on the patient's chest and abdomen while a fourth detector is placed on the CT or treatment couch for reference. Position coordinates of the sensors are read in real‐time at a rate of 20–25 Hz and total dose is read at the end of each treatment fraction. Measurements have been completed on 11 patients during the 4DCT and 7–16 treatment fractions. The standard deviation of the average dose measured at each point ranged from 3.0 to 13.7 cGy (7.7 to 14.0%) at CT zero and from 2.5 to 11.1 cGy (2.8 to 9.2%) at the site of the tumour. Variations in amplitude of breathing motion have been found to be patient‐specific. Some patients had very consistent breathing patterns, with interfraction variations in average amplitude and period as low as 11.4% and 4.2% respectively, while others had variations as high as 38.9% and 50.0% . Daily set‐up of the RADPOS system was completed quickly, requiring minimal additional time for each scheduled treatment fraction. Acknowledgements: This project is supported by grants from HTX and ORCC Foundation. Financial and technical support from Best Medical Canada is also acknowledged.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".