THE FUTURE BRACHYTHERAPY SUITE, NOW - OPTIMIZING PATIENT CARE THROUGH MULTI-DISCIPLINARY DESIGN AND COLLABORATION
Bibliographic record
Abstract
Image-guided brachytherapy (BT) is an integral part of treatment for many cancers including prostate, gynecologic and ocular. It is both infrastructurally and resource intensive. Therefore, effective design and implementation of an integrated MRI and HDR BT suite with a workflow that would optimize efficiency, safety and quality of care was sought during the build of a new cancer centre. Stakeholder engagement, from detailed design to implementation, involved team members from radiation oncology, medical physics, radiation therapy, anesthesia, nursing, OR management, radiation safety, and patient advocates. Site visits and vendor presentations enabled an understanding of the current landscape of BT infrastructure and workflow around the world. 3D mock-ups enabled simulation of workflows to identify both physical and functional issues that could negatively impact efficiency, safety, and/or quality of care. Equipment training and workflow simulations involving staff from many different disciplines prior to open-to-service helped to increase staff comfort and competency in this complex environment. A single stationary MRI flanked by two operating suites was ultimately designed to increase capacity and access for image guided BT. An integrated translatable tabletop from the surgical bed onto the mobile MRI docking table allows for seamless patient transfer into the MRI with continuous and safe anesthesia. Working with new models of care (including integration of respiratory therapists and anesthesia assistants into the care team) allows the team to run both operating suites more efficiently without compromising patient care or safety. Careful planning and thoughtful design for an integrated MRI and HDR BT suite has resulted in infrastructure and workflow that will future-proof this essential cancer treatment. The form of the design carefully followed the complex function of this space that requires expertise and collaboration among many disciplines to optimize patient care and safety.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".