Outcomes in the Management of Sternal Dehiscence by Plastic Surgery
Bibliographic record
Abstract
In Brief Purpose: Infection rates following median sternotomy vary between 0.2% and 10%. These cases are associated with morbidity and mortality rates between 10% and 25% and 5% and 20%, respectively. The purpose of this study was to evaluate patient outcomes following plastic surgery correction of sternotomy dehiscence (SD). Methods: All patients operated on for an SD following coronary artery bypass graft surgery (CABG), between 1995 and 2005, with 1 or more flaps, were included. Results: Eighty cases were identified over a 10-year period. The mean age was 64 (±9.1) years. Two or more procedures were required in 17.5% of patients, and the mortality rate within 30 days was 12.5%. Significant variability was revealed between the cumulative mortality rates of plastic surgeons, from 0.0% to 50.0%. Multiple associations were identified for poor outcome, including chronic renal insufficiency and early mortality, and obesity with risk of reintervention. Conclusion: Although patients who undergo surgical correction of a deep sternal infection usually tolerate their intervention well, the mortality within 30 days remains high. This study has identified several factors explaining morbidity and mortality in this patient population. A review of flap reconstructions of 80 post-sternotomy infections demonstrated the need for multiple procedures in 17.5% and a 30-day mortality rate of 12.5%. Obesity was a risk factor for reoperation, and chronic renal failure for mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".