The Impact of Prior Abdominal Surgery on Complications of Abdominally Based Autologous Breast Reconstruction: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background Approximately half of all patients presenting for autologous breast reconstruction have abdominal scars from prior surgery, the presence of which is considered by some a relative contraindication for abdominally based reconstruction. This meta-analysis examines the impact of prior abdominal surgery on the complication profile of breast reconstruction with abdominally based free tissue transfer. Methods Literature search was conducted using PubMed, Scopus, and Web of Science. Included studies examined patients with a history of prior abdominal surgery who then underwent abdominally based free flap breast reconstruction. Prior liposuction patients and those with atypical flap designs were excluded. The Newcastle-Ottawa Scale was used to assess study quality. Flap complications included total and partial flap loss, fat necrosis, infection, and reoperation. Donor-site complications included delayed wound healing, infection, seroma, hematoma, and abdominal wall morbidity (hernia, bulge, laxity). Relative risk and 95% confidence intervals (CIs) between groups were calculated. Forest plots, I 2 statistic heterogeneity assessments, and publication bias funnel plots were produced. Publication bias was corrected with a trim-and-fill protocol. Overall effects were assessed by fixed-effects and random-effects models. Results After inclusion and exclusion criteria were applied, 16 articles were included for final review. These included 14 cohort and 2 case–control studies, with 1,656 (46.3%) patients and 2,236 (48.5%) flaps having undergone prior surgery. Meta-analysis showed patients with prior abdominal surgery were significantly more likely to experience donor-site delayed wound healing with a risk ratio of 1.27 (random 95% CI [1.00; 1.61]; I 2= 4) after adjustment for publication bias. No other complications were statistically different between groups. Conclusion In patients with a history of prior abdominal surgery, abdominally based free tissue transfer is a safe and reliable option. Abdominal scars may slightly increase the risk of delayed donor-site wound healing, which can aid the surgeon in preoperative counseling.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.003 | 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".