The Duration of Hypotension before the Initiation of Antibiotic Treatment Is a Critical Determinant of Survival in a Murine Model of<i>Escherichia coli</i>Septic Shock: Association with Serum Lactate and Inflammatory Cytokine Levels
Bibliographic record
Abstract
BACKGROUND: This study was designed to examine the relationship between the timing of antibiotic treatment and both survival rates and hemodynamic/inflammatory correlates of survival in a murine model of Escherichia coli septic shock. METHODS: Surgical implantation of an E. coli (O18:K1:H7)-laced, gelatin capsule-encased fibrinogen clot was used to generate a bacteremic model of murine septic shock. Survival duration, hemodynamic responses, and circulating serum tumor necrosis factor (TNF)-alpha , interleukin (IL)-6, and lactate levels were assessed in relation to increasing delays in or absence of antibiotic treatment. RESULTS: A critical inflection point with respect to survival occurred between 12 and 15 h after implantation. When initiated at or before 12 h, antibiotic treatment resulted in < or = 20% mortality, but, when initiated at or after 15 h, it resulted in >85% mortality. Physiologically relevant hypotension developed in untreated septic mice by 12 h after implantation. Values for heart rate differed between untreated septic mice and sham-infected control mice by 6 h after implantation, whereas values for cardiac output and stroke volume did not differ until at least 18-24 h after implantation. Antibiotic treatment initiated > or = 12 h after implantation was associated with persistence of increased circulating serum lactate, TNF- alpha , and IL-6 levels. CONCLUSIONS: The timing of antibiotic treatment relative to hypotension is closely associated with survival in this murine model of septic shock. Delay in antibiotic treatment results in the persistence of inflammatory/stress markers even after antibiotic treatment is initiated.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".