Poster — Wed Eve—03: Delivering Brachytherapy for Cervical Cancer: Organizational and Technical Advice to Facilitate Care
Bibliographic record
Abstract
Many centres in Ontario are currently starting programs in HDR brachytherapy for cervical cancer. To provide guidance for establishing such programs, an Expert Working Group (EWG) was struck in collaboration with Cancer Care Ontario's Program in Evidence Based Care (PEBC) to examine the technical and organizational requirements for high‐quality delivery of HDR to women with cervical cancer. All components of brachytherapy were considered including required facilities, imaging technologies, team composition and qualifications, treatment planning, caseload/volumes, documentation, and quality control. Organizational recommendations for each of these areas were developed based on evidence gathered through an environmental scan and a systematic search of the literature. As such, they represent a synthesis, with adaptation to the environment in Ontario, of the most recent, comprehensive, and relevant existing documents combined with expert consensus opinion of the panel. Although designed for Ontario, these recommendations are generally applicable to other jurisdictions looking to put in place new brachytherapy services, or to improve existing ones. A particular focus of the EWG was the examination of evidence supporting the use of different imaging technologies for guiding and planning HDR brachytherapy of the cervix. Imaging is a key component of this technique and optimizing its use may improve both the quality of brachytherapy and treatment outcomes. We present the first systematic review of imaging for cervical brachytherapy. Although the studies reviewed varied in design and quality, all favoured planning with volumetric 3D imaging over conventional 2D imaging.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".