Poster - Thurs Eve-02: 3 dimensional ultrasound-guided breast brachytherapy
Bibliographic record
Abstract
Breast cancer is one of Canada's leading causes of death, taking the lives of approximately 5000 people annually. Breast-conserving tumour excision, or lumpectomy followed by radiation therapy is becoming an increasingly common treatment method for smaller tumours. High dose rate (HDR) brachytherapy is a precise form of radiation delivery following surgery involving the delivery of radiation dose through an HDR afterloader attached to catheters inserted into the breast. Currently, a CT scan of the patient is taken to properly reconstruct the tumour and guide catheter insertion. We propose to use our three-dimensional ultrasound (3DUS) scanner as the primary treatment planning device, eliminating the need for a CT scan. This would greatly increase patient comfort along with saving time and money. We have designed and constructed a 3DUS scanner specifically to be used in breast brachytherapy. It attaches to a Kuske breast application kit already used in the clinic. Software to view 3DUS images produced by the device is already being used, and needle guidance software is currently being developed. Laboratory tests on agar phantoms are set to begin shortly to evaluate the precision of the device and perform brachytherapy catheter insertion simulations. When the results of these tests are satisfactory, a full brachytherapy procedure will be performed in Quebec City using the 3DUS scanner.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 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".