Are high mastectomy rates associated with unwanted variation in other aspects of breast cancer treatment? An overview of practice patterns in Alberta, Canada in 2009-2010.
Bibliographic record
Abstract
102 Background: The Canadian Partnership Against Cancer recently released a system performance report identifying large variation in breast cancer treatment across Canada. Contrary to provincial guidelines, Alberta had higher than average rates of mastectomy (56%); however, factors driving this variation remain unknown. We sought to (1) determine if practice patterns of primary breast surgery and adjuvant therapy adhered to guidelines and (2) to describe influencing factors on treatment practices between the two major cancer programs in Alberta to inform future knowledge translation strategies for guideline implementation. Methods: All patients diagnosed with breast cancer (ICD 9-174) between January 2009 and December 2010 were identified from the Alberta Cancer Registry. Demographic, surgical, treatment, and pathology data were abstracted from the electronic health record. Descriptive statistics and t-testing comparing mastectomy rates, axillary surgery and adjuvant therapy between the two major cancer programs were performed. Results: There were 2,817 surgeries for early breast cancer in the study cohort between 2009 and 2010. Conclusions: Practice patterns identify variance from current guidelines. Mastectomy rates are influenced by surgeon volume and tumor size. Management of positive SN and adjuvant therapy is variable and may reflect under treatment. Further investigation of drivers for mastectomy and adjuvant therapy are necessary. [Table: see text]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".