The Specific Organism: Not Bacterial Gram Type: Drives the Inflammatory Response in Septic Shock
Why this work is in the frame
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Bibliographic record
Abstract
<b><i>Background and Hypothesis:</i></b> The inflammatory response was targeted by unsuccessful therapies but ignored pathogen. We hypothesized that the inflammatory response differs according to organism in human septic shock. <b><i>Materials and Methods:</i></b> We measured 39 cytokines at baseline and 24 h in patients (<i>n</i> = 363) in the Vasopressin and Septic Shock Trial (VASST). We compared cytokine profiles (cytokine functional class) at baseline and at 24 h by organism and used hierarchical clustering to classify cytokines according to 28-day outcomes. <b><i>Results:</i></b> In 363 patients, 88 and 176 patients had at least 1 species isolated from blood and other sites, respectively. Cytokine levels differed significantly according to organism: <i>Neisseria meningitidis</i> and <i>Streptococcus pneumoniae</i> had the highest (baseline and at 24 h), while <i>Enterococcus faecalis</i> (blood) had the lowest mean cytokine levels. <i>N. meningitidis</i> and <i>Klebsiella pneumoniae</i> had significantly higher cytokine levels at baseline versus 24 h (<i>p</i> = 0.01 and 0.02, respectively); <i>E. faecalis</i> had significantly higher cytokine levels at 24 h versus baseline. Hierarchical clustering heat maps showed that pathogens elicited similar cytokine responses not related to the functional cytokine class. <b><i>Conclusion:</i></b> The organism type induces different cytokine profiles in septic shock. Specific gram-positive and gram-negative pathogens stimulated similar plasma cytokine-level patterns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it