A Review of 559 Sternal Wound Reconstructions at a Single Institution
Bibliographic record
Abstract
BACKGROUND: Sternal wound infection (SWI) and dehiscence after median sternotomy for cardiac surgery remain challenging clinical problems with high morbidity. Bilateral pectoralis major myocutaneous flaps are excellent for most sternal wounds but do not reach deeper mediastinal recesses. The omental flap may be a useful adjunct for addressing these deeper mediastinal infections. METHODS: Records of 598 sternal wound reconstructions performed by a single surgeon (J.A.A.) from 1996 to 2022 were reviewed. At the time of surgery, patients underwent sternal hardware removal, debridement, and closure with bilateral pectoralis major myocutaneous flaps. Pedicled omental flaps were also mobilized when additional vascularized tissue was required within the deeper mediastinum. RESULTS: Complete data were available for 559 sternal wound reconstructions performed by the senior author during this period. Bilateral pectoralis and omental flaps were mobilized in 17 of 559 (3.04%) patients. Common indications for initial cardiac surgery included repair or replacement of diseased aortic roots (9/17; 52.94%), aortic valves (8/17; 47.06%), and mitral valves (6/17; 35.29). Mean American Society of Anesthesiologists score was 3.56. Preoperative morbidity included culture-positive wound infection (12/17; 70.59%), dehiscence (15/17; 88.24%), wound drainage (11/17; 64.71%), and inability to close the chest after the original sternotomy because of hemodynamic instability (6/17; 35.29%). Intraoperative deep mediastinal or bone cultures were positive in 8 of 17 (47.06%) patients. Postoperative complications included partial dehiscence (2/17; 11.76%), skin edge necrosis (1/17; 5.88%), seroma (1/17; 5.88%), abdominal hernia (1/17; 5.88%), and recurrent infection (2/17; 11.76%). Three patients (17.65%) died within 30 days of the reconstruction surgery. CONCLUSIONS: Patients undergoing combined pectoralis major and omental flap closure frequently had a history of aortic root and valve disease, and other significant preoperative morbidities. However, postoperative complication rates after combined flap closure were relatively low. Combined pectoralis major and omental flap reconstruction thus appears to be an effective intervention in patients with sternal wounds extending into the deep mediastinum.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".