Predicting Complications in Immediate Alloplastic Breast Reconstruction: How Useful Is the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator?
Bibliographic record
Abstract
BACKGROUND: Complications following immediate breast reconstruction can have significant consequences for the delivery of postoperative chemotherapy and radiation therapy. Identifying patients at higher risk of complications would ensure that immediate breast reconstruction does not compromise oncologic treatment. The American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator is an online tool in the public domain that offers individualized preoperative risk prediction for a wide range of surgical procedures, including alloplastic breast reconstruction. This study evaluates the usefulness of this tool in patients undergoing immediate breast reconstruction with tissue expanders at a single institution. METHODS: Details of 278 patients who underwent immediate breast reconstruction with tissue expander placement were entered into the calculator to determine the predicted complication rate. This was compared to the rate of observed complications on chart review. The predictive model was evaluated for calibration and discrimination using the statistical measures used in the original development of the calculator. RESULTS: The predicted rate of complications (5.2 percent) was significantly lower that the observed rate (16.2 percent; p < 0.01). The Hosmer-Lemeshow test confirmed lack of fit of the model. The C statistic was 0.62 and the Brier score was 0.173, indicating that the model had poor predictive power and could not discriminate between those who were at risk for complications and those who were not. CONCLUSIONS: The American College of Surgeons National Surgical Quality Improvement Program universal Surgical Risk Calculator underestimated the proportion of patients that would develop complications in this cohort. In addition, it was unable to effectively identify individual patients at increased risk, suggesting that this tool would not make a useful contribution to preoperative decision-making in this patient group.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".