Reduced Hospital Costs for Ultrasound-guided Vacuum-assisted Excision Compared with Open Surgery in Patients with Benign Breast Masses and High-risk Lesions
Bibliographic record
Abstract
OBJECTIVE: Benign breast masses represent a substantial proportion of breast cancer screening results and may require multiple follow-up visits and biopsy. Even with a preceding benign core biopsy, benign masses have been excised via open surgery for a variety of reasons. This study compared the procedural costs of US-guided vacuum-assisted excision (US-VAE) versus open surgical excisions for benign breast masses and high-risk lesions (HRL). METHODS: In this retrospective cohort study, female outpatients receiving US-VAE or open excision of benign breast masses between 2015 and 2018 were identified within the Premier Healthcare Database. A secondary analysis was conducted for patients with HRLs. Propensity score matching and multivariate regression adjusted for patient demographics, encounter level covariates, and hospital characteristics. The total procedural costs were reported from a hospital perspective. RESULTS: A total of 33 724 patients underwent excisions for benign breast masses (8481 US-VAE and 25 242 open surgery). Procedural costs were significantly lower in unmatched patients who received US-VAE ($1350) versus open surgery ($3045) (P < 0.0001). After matching, a total of 5499 discharges were included in each group, with similar findings for US-VAE ($1348) versus open surgery ($3101) (P < 0.0001). A secondary analysis of matched HRL patients (41 discharges in each group) also showed significantly lower procedural costs with US-VAE ($1620) versus open surgery ($3870) (P < 0.0001). CONCLUSION: Among patients with benign breast masses or HRLs, US-VAE was associated with significantly lower procedural costs versus open surgery. If excision is performed and expected clinical outcomes are equal, US-VAE is preferable to reduce costs without compromising the quality of 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".