Reoperation Costs in Attempted Breast-Conserving Surgery: A Decision Analysis
Bibliographic record
Abstract
BACKGROUND: Breast-conserving surgery (bcs) is the preferred surgical approach for most patients with early-stage breast cancer. Frequently, concerns arise about the pathologic margin status, resulting in an average reoperation rate of 23% in Canada. No consensus has been reached about the ideal reoperation rate, although 10% has been suggested as a target. Upon undergoing reoperation, many patients choose mastectomy and breast reconstruction, which add to the morbidity and cost of patient care. We attempted to identify the cost of reoperation after bcs, and the effect that a reduction in the reoperation rate could have on the B.C. health care system. METHODS: A decision tree was constructed to estimate the average cost per patient undergoing initial bcs with two reoperation frequency scenarios: 23% and 10%. The model included the direct medical costs from the perspective of the B.C. health care system for the most common surgical treatment options, including breast reconstruction and postoperative radiation therapy. RESULTS: Costs ranged from a low of $8,225 per patient with definitive bcs [95% confidence interval (ci): $8,061 to $8,383] to a high of $26,026 for reoperation with mastectomy and delayed reconstruction (95% ci: $23,991 to $28,122). If the reoperation rate could be reduced to 10%, the average saving would be $1,055 per patient undergoing attempted bcs (95% ci: $959 to $1,156). If the lower rate were to be achieved in British Columbia, it would translate into a savings of $1.9 million annually. SUMMARY: The implementation of initiatives to reduce reoperation after bcs could result in significant savings to the health care system, while potentially improving the quality of patient care.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".