Improved Operative Efficiency and Surgical Times in Autologous Breast Reconstruction: A 15-year Single-center Retrospective Review
Bibliographic record
Abstract
Background: Autologous breast reconstruction using a free deep inferior epigastric perforator (DIEP) flap is a complex procedure that requires a dedicated approach to achieve operative efficiency. We analyzed data for DIEP flaps at a single center over 15 years to identify factors contributing to operative efficiency. Methods: A single-center, retrospective cohort analysis was performed of consecutive patients undergoing autologous breast reconstruction using DIEP free flaps between January 1, 2005, and December 31, 2019. Data were abstracted a priori from electronic medical records. Analysis was conducted by a medical statistician. Results: Analysis of 416 unilateral and 320 bilateral cases (1056 flaps) demonstrated reduction in operative times from 2005 to 2019 (11.7–8.2 hours for bilateral and 8.4–6.2 hours for unilateral, P < 0.000). On regression analysis, factors significantly correlating with reduced operative times include the use of venous couplers ( P < 0.000), and the internal mammary versus the thoracodorsal recipient vessels ( P < 0.000). Individual surgeon experience correlated with reduced OR times. Post-operative length of stay decreased significantly, without an increase in 30-day readmission or emergency presentations. Flap failure occurred in two cases. Flap take-back rate was 2% (n = 23) with no change between 2005 and 2019. Conclusions: Operative times for breast reconstruction have decreased significantly at this center over 15 years. The introduction of venous couplers, use of the internal mammary system, and year of surgery significantly correlated with decreased operative times. Surgeon experience and a shift in surgical workflow for DIEP flap reconstruction likely contributed to the latter finding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".