Co-surgeon versus Single-surgeon Outcomes in Free Tissue Breast Reconstruction: A Meta-analysis
Bibliographic record
Abstract
Abstract Background Autologous breast reconstruction offers superior long-term patient reported outcomes compared with implant-based reconstruction. Universal adoption of free tissue transfer has been hindered by procedural complexity and long operative time with microsurgery. In many specialties, co-surgeon (CS) approaches are reported to decrease operative time while improving surgical outcomes. This systematic review and meta-analysis synthesizes the available literature to evaluate the potential benefit of a CS approach in autologous free tissue breast reconstruction versus single-surgeon (SS). Methods A systematic review and meta-analysis was conducted using PubMed, Embase, and MEDLINE from inception to December 2022. Published reports comparing CS to SS approaches in uni- and bilateral autologous breast reconstruction were identified. Primary outcomes included operative time, postoperative outcomes, processes of care, and financial impact. Risk of bias was assessed and outcomes were characterized with effect sizes. Results Eight retrospective studies reporting on 9,425 patients were included. Compared with SS, CS approach was associated with a significantly shorter operative time (SMD −0.65, 95% confidence interval [CI] −1.01 to −0.29, p < 0.001), with the largest effect size in bilateral reconstructions (standardized mean difference [SMD] −1.02, 95% CI −1.37 to −0.67, p < 0.00001). CS was also associated with a significant decrease in length of hospitalization (SMD −0.39, 95% CI −0.71 to −0.07, p = 0.02). Odds of flap failure or surgical complications including surgical site infection, hematoma, fat necrosis, and reexploration were not significantly different. Conclusion CS free tissue breast reconstruction significantly shortens operative time and length of hospitalization compared with SS approaches without compromising postoperative outcomes. Further research should model processes and financial viability of its adoption in a variety of health care models.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.055 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".