Factors Influencing the Rate of Post-Mastectomy Breast Reconstruction in a Canadian Teaching Hospital
Bibliographic record
Abstract
BACKGROUND: Post-mastectomy breast reconstruction (PMBR) improves psychosocial well-being, quality of life, and body image. Reconstruction rates vary widely (up to 42% in the United States), but the few Canadian studies available report rates of 3.8% to 7.9%. We sought to evaluate the current state of breast reconstruction in 1 Canadian teaching hospital and factors determining patients' access to reconstruction. METHODS: We performed a retrospective chart review of all patients with breast cancer undergoing mastectomy alone or mastectomy and reconstruction at a Canadian hospital between 2010 and 2013. We calculated rates of breast reconstruction and compared patient characteristics between the 2 groups, and then performed a multiple logistic regression to determine factors increasing the odds of receiving breast reconstruction. RESULTS: A total of 152 patients underwent 154 total or modified radical mastectomies. We obtained a rate of PMBR of 21%, 14% immediate reconstruction, and 8% delayed. Statistical analysis showed that compared to patients with mastectomy alone, patients who received PMBR were significantly younger, with a larger percentage having bilateral mastectomies, non-invasive breast cancer, and residing further from the hospital. Patients less than 50 years old and those with bilateral mastectomies had significantly greater odds of having a reconstruction. CONCLUSIONS: Our Canadian tertiary care institution has a high volume of breast surgery and an active breast reconstruction team. However, the rate of immediate reconstruction remains low compared to similar centers in the United States. We recommend a united effort to increase awareness regarding PMBR and address common misconceptions hindering patients' access to breast reconstruction. LEVEL OF EVIDENCE: Epidemiologic study, Level III.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".