524 Use of Broad-Spectrum Antibiotics and Sepsis-3 Criteria in Burns
Bibliographic record
Abstract
Abstract Introduction Sepsis is a diagnostic challenge in critically ill patients; especially so in the burn population because the signs and symptoms of sepsis are pervasive after injury. The Sepsis-3 criteria identify organ dysfunction as an acute change in SOFA score ≥ 2 points consequent to infection. The objective of this study was to evaluate if Sepsis-3 criteria were fulfilled when broad-spectrum antimicrobial therapy was started in a burn cohort. Methods We included all adult (≥ 18 years) patients with an acute burn admitted to our burn centre within 2 days of injury between 2016 and 2019. Only patients that received meropenem or piperacillin/tazobactam during their acute hospitalization period were included. Patients were stratified based on the Sepsis-3 definition using evidence of infection and evaluation of organ failure in the 48-hour period prior to the administration of antibiotics. Results We studied 70 patients, with 24 patients in the control group and 46 patients in the Sepsis-3 group. Demographics were similar among the control and Sepsis-3 groups: mean age was 44 ± 18 versus 48 ± 18 years (p=0.372); but injury severity was significantly different: median percent TBSA burn 18% vs. 32% (p=0.003) and proportion of inhalation injury 13% vs. 50% (p=0.002). Length of stay (LOS) was significantly longer in the Sepsis-3 group, control group median 23 days vs. median 43 days (p< 0.001). However, LOS/TBSA was not significantly different in the control group compared to the Sepsis-3 group: median 1.6 vs. 1.4 days per percent TBSA burn (p=0.777). Mortality was similar among the groups: 13% vs. 20% (p=0.526). The proportion of patients diagnosed by a physician with sepsis was also similar with 21% in the control group vs. 33% in the Sepsis-3 group (p=0.406). Conclusions Though the Sepsis-3 group had greater injury severity, mortality, and LOS in-hospital, when normalized to TBSA, was similar. Patients were diagnosed by a physician with sepsis in less than a third of cases. This raises the question of why broad-spectrum antibiotics were started. Potentially, patients were treated based on clinical suspicion of sepsis instead of delaying treatment until diagnosis was confirmed. Benefits of early antibiotic administration must be considered in conjunction with antimicrobial stewardship.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".