INTERLEUKIN-8 AS A PREDICTOR OF THE SEVERITY OF BACTEREMIA AND INFECTIOUS DISEASE
Bibliographic record
Abstract
The relationship between cytokines and sepsis has been studied frequently in the intensive care unit (ICU). However, the determination of cytokines in patients as they enter the emergency department (ED) would be more meaningful in predicting the outcome of infection. This study investigated plasma interleukin-8 in the ED as the predictor of bacteremia and sepsis. One hundred patients admitted through the ED with signs of systemic inflammatory response syndrome were studied. Plasma IL-8, IL-6, and tumor necrosis factor (TNF) were assayed by enzyme-linked immunosorbent assay. Patient's data were evaluated using the APACHE II scoring system as predictive factors of morbidity and mortality. Plasma IL-8 (149 pg/mL) detected bacteremia with a positive predictive value of 90.9% and a specificity of 98.7%. Results indicated that the odds ratios (ORs) of bacteremia were 24.78 (P < 0.01, CI = 2.27-270.8), 5.42 (P < 0.05, CI = 1.37-21.4), and 6.05 (P < 0.05, CI = 1.36-26.8) for IL-8, IL-6, and APACHE II, respectively. Occurrence of bacteremia was highly correlated with increases in plasma IL-8 (P < 0.01). IL-8 (OR = 8.25, CI = 1.03-65.9) and APACHE II scores (OR = 12.6, CI = 2.24-70.4) were found to be significantly better predictive factors of mortality (P < 0.01) than IL-6 (OR = 3.60, CI = 0.57-22.7), TNF (OR = 0.24, CI = 0.01-11.0) and age (OR = 1.02, CI = 0.98-1.06). During bacteremia, IL-8 also correlated well with patient use of a ventilator (P < 0.01, OR = 2.43, CI = 2.41-311.19), use of vasopressors (P < 0.05, OR = 2.67, CI = 1.79-370.78), length of stay in the hospital (P < 0.01, OR = 3.14, CI = 1.87-988.31), and stay in the ICU (P < 0.01, OR = 2.51, CI = 2.98-449.80). Measuring IL-8 on patients in the ED with apparent bacterial infections appears to be a reliable predictor of bacteremia and the severity of disease.
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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) | 0.000 | 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 | 0.000 |
| 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".