Titanium plate fixation versus conventional approach in the treatment of deep sternal wound infection
Bibliographic record
Abstract
BACKGROUND: Deep sternal wound infection (DSWI) is a serious complication post cardiac surgery and associated with increased mortality, morbidity and cost. Sternal titanium plate fixation could be an effective approach to treat DSWI. We sought to compare the effectiveness of titanium plate fixation with conventional approach in the treatment of DSWI. METHODS: Retrospective data was analyzed from consecutive patients with DSWI post cardiac surgery who received either titanium plate fixation (sternal plate group) or conventional treatment with sternal debridement and rewiring (control group). Pre-operative risk factors and post-operative clinical outcome were compared between the 2 groups. RESULTS: A total of 36 patients (mean age 65.0 ± 8.6, 63.9 % male) with DSWI were in the sternal plate group whereas 26 patients (mean age 64.0 ± 13.4, 65.4 % male) were in the control group. The mean follow-up period was 15.92 months. The major pre-operative comorbidities were comparable between the 2 groups. The rate of receiving multiple debridement procedures (≥ 3) was significantly lower in the sternal plate group (5.6 % vs. 26.9 %, P = 0.03). Patients in the sternal plate group had no treatment failure, whereas 42.3 % of patients in the control group had treatment failure requiring muscle flaps reconstruction by plastic surgery (0 % vs. 42.3 %, P < 0.001). There was a trend of lower in-hospital mortality (11.1 % vs. 19.2 %, P = 0.47) in the sternal plate group. CONCLUSION: Compared to conventional treatment, titanium plate fixation appears to have favorable clinical outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".