Surgical treatment choices for breast cancer in Newfoundland and Labrador: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Mastectomy is often chosen by women for treatment of breast cancer, even when breast-conserving surgery (BCS) is an option. Newfoundland and Labrador has a high mastectomy rate. We documented the number of breast cancers over a given period in the province and their related surgical treatments, and explored the impact of several variables on surgical choice. METHODS: A retrospective cohort design linked diagnosis data from the Newfoundland and Labrador tumour registry to surgery data from the Canadian Institute for Health Information Discharge Abstract Database. Data were extracted for all women aged 19 years or more in whom breast cancer was diagnosed in 2009-2014. RESULTS: A total of 2346 cases of breast cancer with a linked surgical procedure were included. Most operations (1605 [68.4%]) were mastectomy procedures, with the remainder being BCS. Logistic regression analysis revealed that women were 1.82 times (95% confidence interval [CI] 1.64-2.02) more likely to have mastectomy for each unit of stage increase from 0 to IV and 1.15 times (95% CI 1.11-1.21) more likely for each unit of driving time increase. CONCLUSION: Tumour stage and driving time to a radiation facility significantly predicted Newfoundland and Labrador women's surgical treatment choices for breast cancer. Notably, mastectomy was the favoured choice across all age groups, tumour stages and geographical regions of the province. We hope that these results will galvanize efforts to better understand local surgical practices and assist in improving the quality of surgical care of women with breast cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".