713 Acute Change in qSOFA Score as a Prognostic Tool for Diagnosing Sepsis in Burn Patients
Bibliographic record
Abstract
Abstract Introduction Sepsis is a diagnostic challenge in all critically ill patients, but particularly so in the burn patient population. The objective of this study was to evaluate if the modified sepsis-3 criteria defined as an acute change in SOFA score ≥2 was predictive of clinical infection. Methods The hospital database was reviewed between 2016 and 2019 to identify patients who received broad spectrum antibiotics within 2 days of sustaining their injury. Culture specimens taken at the time of antibiotic administration determined presence of a clinical infection. Results Between 2016 and 2019, a total of 98 patients were admitted to the burn unit within 2 days of their injury and received prophylactic meropenum and/or piperacillin/tazobactam for suspicion of clinical infection. When stratified based on an acute change in SOFA score within 48 hours prior to receiving antibiotics, 67 (72%) patients received antibiotics with an acute change in SOFA score ≤1, and 26 (27%) patients received antibiotics with an acute change in SOFA score ≥2. Of those patients with the acute change in SOFA score ≥2, 22 patients (85%) had positive cultures associated with the timing of prophylactic antibiotics, as compared to 60 patients (90%) with a change in SOFA score ≤1 (p value 0.05). Conclusions Our data suggests that the modified sepsis-3 criteria is not a reliable tool for diagnosing burn sepsis. Applicability of Research to Practice Improving our ability to accurately predict sepsis in burn patients can improve patient outcomes and decrease unnecessary administration of broad spectrum antibiotics.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".