Geographic Variation Immediate and Delayed Breast Reconstruction Utilization in Ontario, Canada and Plastic Surgeon Availability: A Population‐Based Observational Study
Bibliographic record
Abstract
BACKGROUND: Utilization of breast reconstruction (BR) is low in many jurisdictions. We studied the geographical and surgical workforce factors that contribute to access and use of BR using a small area analysis approach with a geographical unit of analysis. METHODS: We linked administrative data from Ontario Canada to calculate the age-standardized rates for immediate BR (IBR) (same time as mastectomy) between 2002 and 2011, and delayed BR (DBR) (within 3 years of mastectomy) for each county. The influence of plastic surgeon access on variation in county rates of BR was examined using Poisson random effects models. RESULTS: 12,663 women underwent mastectomy in Ontario; 2,948 had BR within 3 years (23.3%). Over 50% of the counties had no access to any plastic surgeon. County IBR rates ranged from 0 to 21.5%; plastic surgeon access explained 46% of geographic variation (p<0.0001). IBR rates in counties with very low, low, and moderate access to plastic surgeons were significantly less than counties with high access (relative rate [RR] 0.48 [95% confidence interval (CI) 0.35-0.66], RR 0.61 [CI 0.43-0.87] and RR 0.70 [CI 0.52-0.96], respectively) after adjusting for age and county socioeconomic characteristics. For DBR, while there was less geographic variation, very low access counties demonstrated reduced rates (RR 0.60 [CI 0.47-0.76]). INTERPRETATION: Geographic access to a plastic surgeon is a major determinant of BR. Targeted interventions for regions without high access to plastic surgeons may improve overall rates and reduce geographic disparities in care, particularly for IBR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".