Sci-Sat AM(2): Brachy - 01: A Quality Assurance Tool for High-Dose-Rate Brachytherapy
Bibliographic record
Abstract
The purpose of this work was to develop a quality assurance (QA) tool for high-dose-rate brachytherapy that would quickly and easily verify both source positioning (dwell positions) and durations (dwell times). We constructed a QA tool that combined radiochromic film to verify position with four photodiode detectors to verify dwell times. To characterise the temporal accuracy of the tool, a function generator powered four red light-emitting diodes that were optically coupled to the four photodiode detectors. Our QA tool was used to verify the dwell positions and times of a commercial brachytherapy afterloader. Measurements of dwell time were independently verified by a one-dimensional optical camera that acquired 1000 lines per second. The temporal accuracy of the QA Tool was found to be about 1 ms. For visual assessment, the source position could be located to within about 0.5 mm. Evaluating the accuracy and precision of an HDR brachytherapy afterloader, we found that the bias in dwell time can exceed 60 ms and the dwell time associated with the first dwell position had an unexpectedly large standard deviation of 30 ms. We found the source locations were much easier to locate on the film if a plastic catheter was used instead of a metal treatment tube. Scanning the films enabled the dwell positions to be determined to within about 0.2 mm. For pre-treatment QA, we found that this tool allowed verification of dwell positions and dwell times in about 6 minutes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.016 | 0.004 |
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".