Open Reduction Internal Fixation Poststernotomy Mediastinitis
Bibliographic record
Abstract
Introduction. Mediastinitis has been reported to complicate 5% of sternotomy surgery. We have adopted an open reduction and rigid internal fixation (ORIF) approach during the conventional rescue surgery in the treatment of mediastinitis. Methods. A retrospective review was performed to compare the outcomes of patients that had an ORIF to correct postoperative mediastinitis following median sternotomy. These were compared with the outcome of the patients that did not undergo ORIF. Results. In the 5-year study period, we reviewed 35 mediastinitis patient charts. Postoperatively, the ORIF patient group remained in the Intensive Care Unit (ICU) and on a ventilator for a mean of 1.5 and 0.75 days, respectively. Patients treated without ORIF spent significantly more days in the ICU (mean of 7.5 days, P < 0.05) and on a ventilator (mean of 2.15 days, P = 0.1). Furthermore, it was found that none of the patients (0%) who underwent ORIF complained of any postoperative sternal instability or pain. Preoperatively, however, these rates were as high as 72%. Conclusions. In the select patient, ORIF can be a safe option in the management of mediastinitis, which we have shown to significantly decrease morbidity and mortality by providing anatomic reduction as well as physiologic stabilization. We have shown that ORIF will improve the quality of life of the patient by minimizing abnormal sternal mobility and pain and will also decrease inpatient costs by decreasing days spent in the ICU and ventilator dependence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".