Re-imagining breast care: Cost-effective innovations for Canadian healthcare
Bibliographic record
Abstract
With an anticipated increase in breast screening volume, provincial healthcare systems and health leaders must identify innovative technologies and care pathways that can alleviate the burden of an already resource-constrained healthcare system. The solution explored here utilizes vacuum-assisted technology that is clinically equivalent and a more cost-effective alternative care pathway, as successfully demonstrated in many other countries. This article reviews the clinical efficacy of Vacuum-Assisted Biopsy (VAB) and Vacuum-Assisted Excision (VAE) and calculates the potential Canadian direct cost savings by implementing VAE for the management of benign and high-risk breast lesions in place of Surgical Diagnostic Excision (SDE): calculated to be $1,607,769 to $11,341,107 (2025 CAD) annually in Canada, or $2,208 (2025 CAD) per-patient procedural savings from avoiding SDEs. Additional non-quantifiable patient benefits are also explored: avoiding unnecessary surgery; preventing the associated anxiety and time off work; and greater patient autonomy over their diagnosis journey, helping maintain their quality of life. Finally, barriers to adoption are identified, and an Implementation Leadership Action Plan is proposed, to help support the successful integration of this practice shift. The plan includes procedural reimbursement and policy changes, and multidisciplinary engagement targeting radiology, surgery, and pathology stakeholders.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".