Development and validation of a risk stratification model for immediate microvascular breast reconstruction
Bibliographic record
Abstract
BACKGROUND: Immediate breast reconstruction has many advantages but is associated with higher complication rates than delayed reconstruction. Complications can delay the delivery of adjuvant cancer treatments. This study aimed to develop and validate a risk stratification model for the prediction of perioperative complications in immediate microvascular breast reconstruction. METHODS: The association between patient and treatment variables and perioperative complications was evaluated in a retrospective cohort of 351 women undergoing immediate breast reconstruction using free deep inferior epigastric artery perforator flaps. Multivariable logistic regression was used to determine the strength of association and weighted scores were assigned. Using cumulative risk scores, patients were stratified into low, intermediate, and high-risk groups. The model was then validated in a prospective cohort of 100 consecutive patients. RESULTS: Obesity, smoking, prior radiation, and comorbidities were important predictors and incorporated into the risk model. Complications occurred in 23.5% of low-risk (95% confidence interval [CI] = 17.7-29.2), 38.4% of intermediate-risk (95% CI = 29.2-47.5) and 53.9% of high-risk (95% CI = 33.3-74.4) patients. Validation confirmed a linear relationship between the risk stratification categories and complications in a model with good predictive power (c-statistic = 0.7, 95% CI = 0.6-0.8). CONCLUSION: A simple risk score, based on known preoperative variables, provides accurate risk stratification for patients considering immediate microvascular breast reconstruction.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".