Geographic variation in breast reconstruction surgery after mastectomy for females with breast cancer in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most common cancer affecting females in Canada, and about half of females with breast cancer are treated with mastectomy. We sought to evaluate geographic variation in breast reconstruction surgery in Alberta, Canada. METHODS: Using linked population-based administrative databases, we extracted data on all Alberta females aged 18 years and older who were diagnosed with breast cancer and treated with mastectomy during 2004-2017. Analyses included regression modelling of odds of reconstruction at 1 year and a spatial scan to identify geographic clusters of lower numbers of reconstruction. RESULTS: A total of 16 198 females diagnosed with breast cancer were treated with a mastectomy, and 1932 (11.9%) had reconstruction within 1 year postmastectomy. Those with reconstruction were more likely to be younger (adjusted odds ratio [OR] 16.7, 95% confidence interval [CI] 13.7-20.3; aged 21-44 yr v. ≥ 65 yr) and were less likely to be from lower-income neighbourhoods. They were more likely to have at least 1 comorbidity and were more likely to have advanced stages of cancer and to require chemotherapy (adjusted OR 0.55, 95% CI 0.47-0.65) or radiotherapy after mastectomy (adjusted OR 0.59, 95% CI 0.39-0.87) than females without reconstruction. We identified rural northern and southeastern clusters with frequencies of reconstruction that were 69.6% and 41.6% of what was expected, respectively. CONCLUSION: We found an overall postmastectomy rate of breast reconstruction of 11.9%, and we identified geographic variation. Predictors of reconstruction in Alberta were similar to those previously described in the literature, specifically with patients in rural communities having lower rates of reconstruction than their urban counterparts. These results suggest that further interventions are required to identify the specific barriers to reconstruction within rural communities and to create strategies to ensure equitable access to all residents.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".