Effects of adjuvant hyperbaric oxygen therapy and real-time fluorescent imaging on deep sternal wound infection: a retrospective study
Bibliographic record
Abstract
OBJECTIVE: Deep sternal wound infection (DSWI) is a rare but devastating complication that is estimated to occur in 1-2% of patients after median sternotomy. Current standard of care (SoC) comprises antibiotics, debridement and negative pressure wound therapy (NPWT). Hyperbaric oxygen therapy (HBOT) appears to be an effective adjuvant therapy for osteomyelitis. The aim of this study was to determine the effectiveness of HBOT and real-time fluorescence imaging (RTFI) in a DSWI treatment protocol and their benefits on infection control. METHOD: A retrospective analysis of DSWI management was performed. Enrolled patients were divided into two groups: HBOT group and RTFI group. Patients in the HBOT group received SoC, HBOT, NPWT and reconstructive flap surgery. Patients in the RTFI group received the same therapeutic plan as well as treatment with a RTFI device (MolecuLight i:X (MolecuLight, Inc., Canada) to achieve high-quality debridement. Infection status and short-term outcomes within three months were measured. Long-term outcomes were analysed at a 12-month follow-up. RESULTS: Of the 55 patients enrolled: 22 in the HBOT group and 33 in the RTFI group. Infection control status, evaluated in terms of white blood cell counts and C-reactive protein levels, antibiotic use duration, antibiotic costs, reinfection rate and osteomyelitis recurrence rate, were statistically significantly improved in the RTFI group (<0.001, <0.001, 0.042, 0.022, 0.049 and 0.022, respectively). Length of total intensive care unit stay and duration of complete healing were statistically significantly decreased in the RTFI group (<0.001 and 0.046, respectively). CONCLUSION: Patients with DSWI can benefit from HBOT, especially in terms of in-hospital mortality. RTFI can be used to eliminate bacterial burden and achieve high-quality debridement, which considerably improves infection control and clinical outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".