The Modified Sternoplasty: A Novel Surgical Technique for Treating Mediastinitis
Bibliographic record
Abstract
Deep sternal wound infection (DSWI) is one of the most complex and devastating complications post cardiac surgery. We present here the modified sternoplasty, a novel surgical technique for treating DSWI post cardiac surgery. The modified sternoplasty includes debridement and sternal refixation via bilateral longitudinal stainless-steel wires that are placed parasternally along the ribs at the midclavicular or anterior axillary line, followed by six to eight horizontal stainless-steel wires that are anchored laterally and directly into the ribs. On top of that solid structure, wound reconstruction is performed by the use of bilateral pectoralis muscle flaps followed by subcutaneous tissue and skin closure. We reported mortality rates and length of hospitalization of patients who underwent the modified sternoplasty. In total, 68 patients underwent the modified sternoplasty. Two of these critically ill patients died (2.9%). The average length of hospitalization from the diagnosis of DSWI was 24.63 ± 22.09 days. The modified sternoplasty for treating DSWI is a more complex surgery compared with other conventional sternoplasty techniques. However, this technique was demonstrated to be more effective, having a lower rate of mortality, and having a length of hospitalization lower than or comparable to other techniques previously reported in the literature.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".