Poster — Thur Eve — 40: Automated Quality Assurance for Remote‐Afterloading High Dose Rate Brachytherapy
Bibliographic record
Abstract
High dose rate (HDR) remote afterloading brachytherapy involves sending a small, high‐activity radioactive source attached to a cable to different positions within a hollow applicator implanted in the patient. It is critical that the source position within the applicator and the dwell time of the source are accurate. Daily quality assurance (QA) tests of the positional and dwell time accuracy are essential to ensure that the accuracy of the remote afterloader is not compromised prior to patient treatment. Our centre has developed an automated, video‐based QA system for HDR brachytherapy that is dramatically superior to existing diode or film QA solutions in terms of cost, objectivity, positional accuracy, with additional functionalities such as being able to determine source dwell time and transit time of the source. In our system, a video is taken of the brachytherapy source as it is sent out through a position check ruler, with the source visible through a clear window. Using a proprietary image analysis algorithm, the source position is determined with respect to time as it moves to different positions along the check ruler. The total material cost of the video‐based system was under $20, consisting of a commercial webcam and adjustable stand. The accuracy of the position measurement is ±0.2 mm, and the time resolution is 30 msec. Additionally, our system is capable of robustly verifying the source transit time and velocity (a test required by the AAPM and CPQR recommendations), which is currently difficult to perform accurately.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.013 |
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".