Assessment of Breast Cancer Surgery in Manitoba: A Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Variation in breast cancer surgical practice patterns can lead to poor clinical outcomes. It is important to measure and reduce variation to ensure all women diagnosed with breast cancer receive equitable, high-quality care. A population-based assessment of the variation in breast cancer surgery treatment and quality has never been conducted in Manitoba. The objective of this study was to assess the variation in surgical treatment patterns, quality of care, and post-operative outcomes for women diagnosed with invasive breast cancer. METHODS: This descriptive study used data from the Manitoba Cancer Registry, Hospital Discharge Abstracts Database, Medical Claims, Manitoba Health Insurance Registry, and Statistics Canada. The study included women in Manitoba aged 20+ and diagnosed with invasive breast cancer between 1 January 2010 and 31 December 2014. RESULTS: Axillary lymph node dissection (ALND) for node-negative disease ranged from 11.8% to 33.3%, timeliness (surgery within 30 days of consult) ranged from 33.3% to 60.2%, and re-excision ranged from 14.7% to 24.6% between health authorities. Women who underwent breast-conserving surgery had the shortest median length of stay and women who underwent mastectomy with immediate reconstruction had the longest median length of stay. In-hospital post-operative complications were higher among women who received mastectomy with immediate reconstruction (9.9%). CONCLUSION: Variation in surgical treatment, quality, and outcomes exist in Manitoba. The findings from this study can be used to inform cancer service delivery planning, quality improvement efforts, and policy development. Influencing data-driven change at the health system level is paramount to ensuring Manitobans receive the highest quality of care.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".