Reoperation cascade in postmastectomy breast reconstruction and its associated factors: Results from a long‐term population‐based study
Bibliographic record
Abstract
BACKGROUND: Unplanned surgeries following postmastectomy breast reconstruction (PMBR) may be required to treat complications or to revise the reconstructed breast. The primary objective of this study was to examine factors that influenced unplanned reoperations after PMBR. METHODS: A retrospective cohort study using provincial databases in Ontario, Canada, was completed. Patients with breast cancer underwent mastectomy between April 2002 and March 2012 followed by immediate or delayed PMBR. Primary outcome was time from PMBR to unplanned reoperations measured in years. The Anderson-Gill counting process model was used to estimate the risk of recurrent unplanned reoperations over time. Univariate and multivariate analyses were completed to examine the association between covariates. RESULTS: A total of 3066 women underwent PMBR and 51.7% had at least one unplanned reoperation. Unplanned breast reoperation was significantly associated with microsurgical tissue vs implant-based reconstruction (hazard ratio [HR]: 1.27), radiation after PMBR (HR: 1.22), surgery at a nonteaching hospital (HR: 1.16), patient comorbidity score (HR: 1.02), and prior unplanned reoperations (HR: 1.25). CONCLUSIONS: Our study provides important long-term population-level data regarding factors influencing unplanned reoperations after PMBR. Patients undergoing microsurgical PMBR or postmastectomy radiation had a higher rate of additional procedures. Every additional reoperation also increases the likelihood of unplanned reoperations resulting in a "reoperation cascade."
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".