Barriers to Immediate Breast Reconstruction in the Canadian Universal Health Care System
Bibliographic record
Abstract
PURPOSE: To describe the population-based rates of immediate breast reconstruction (IBR) for all women undergoing mastectomy for treatment or prophylaxis of breast cancer in the past decade, and to evaluate geographic, institutional, and patient factors that influence use in the publically funded Canadian health care system. METHODS: This population-based retrospective cohort study used administrative data that included 28,176 women who underwent mastectomy (25,141 mastectomy alone and 3,035 IBR) between April 1, 2002, and March 31, 2012, in Ontario, Canada. We evaluated factors associated with IBR by using a multivariable logistic regression model with the generalized estimating equation approach. RESULTS: The population-based, age-adjusted IBR rate increased from 5.1 procedures to 8.7 in 100,000 adult women (43.7%; P < .001), and the increase was greatest for prophylactic mastectomy or therapeutic mastectomy for in situ breast cancer (78.6%; P < .001). Women who lived in neighborhoods with higher median income had significantly increased odds of IBR compared with mastectomy alone (odds ratio [OR], 1.71; 95% CI, 1.47 to 2.00), and immigrant women had significantly lower odds (OR, 0.59; 95% CI, 0.44 to 0.78). A patient had nearly twice the odds of receiving IBR when she was treated at a teaching hospital (OR, 1.84; 95% CI, 1.1 to 3.06) or at a hospital with two or more available plastic surgeons (OR, 2.01; 95% CI, 1.53 to 2.65). Patients who received IBR traveled significantly farther compared with those who received mastectomy alone (OR, 1.04; 95% CI, 1.02 to 1.05 for every 10 km increase). CONCLUSION: IBR is available to select patients with favorable clinical and demographic characteristics who travel farther to undergo surgery at teaching hospitals with two or more available plastic surgeons.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".