Poster ‐ 35: Monitoring patient positioning during deep inspiration breath hold with a distance measuring laser
Bibliographic record
Abstract
Purpose: The accuracy of treatment delivery for left breast/chest wall patients using deep inspiration breath hold (DIBH) is being monitored using a distance measuring laser (DML) Methods: A commercially available DML (DLS‐C15, Dimetix) was mounted behind a Varian TrilogyTM linac. Relative to the machine isocenter, the laser from the beam was offset by 8 cm to the left and by 1 cm in the superior direction. This position was selected because this point is situated on the sternum for the majority of the left breast/chest‐wall patients treated at our institution. The Varian Real‐Time Positioning Management™ (RPM) guided DIBH treatments at our institution is delivered by placing the system's tracking block on the patient's abdomen. The treatment beam is enabled only when the block is in between a predefined abdomen motion range as determined during the CT simulation process. A LabVIEW program was developed to record both beam status (i.e. on/off) and distance measurements. In this study the DML was only used to monitor the position of a single point on the chest and no clinical decisions/adjustments were made based on these measurements. Results and Conclusions: Thus far, 34 fractions have been recorded for 4 patients. As such, the performance of our DIBH treatment technique cannot be fairly evaluated at this point. However, deviations between expected and measured distances have been observed and if these are found to be reproducible, then modifications in our treatment procedures and policies will have to take place.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".