Role of C-reactive Protein and Tumor Necrosis Factor-Alpha in Differentiating between Ventilator-Associated Pneumonia and Systemic Inflammatory Response Syndrome without Infectious Etiology.
Bibliographic record
Abstract
BACKGROUND: Differential diagnosis of systemic inflammatory response syndrome (SIRS) with or without infectious cause is critically important in terms of initiating antimicrobial agents in case of infectious etiology such as ventilator-associated pneumonia (VAP). The aim of this study was to determine the diagnostic and prognostic roles of C-reactive protein (CRP) and tumor necrosis factor-alpha (TNF-α) in differentiating between ventilator-associated pneumonia and SIRS without infectious etiology. MATERIALS AND METHODS: In this prospective observational study, 91 adult intensive care unit (ICU) patients were enrolled. According to established diagnostic criteria, they were classified into three groups of "non-SIRS non-VAP", "SIRS non-VAP" and "SIRS-VAP". Serum CRP and TNF-α were measured on days 1, 3 and 7 of the study and compared using repeated measures ANOVA. RESULTS: With respect to diagnosis, there was no significant difference in the values of these biomarkers between groups (P>0.05). There was no statistically significant "time trend" for C-reactive protein and TNF-α (P>0.05). Considering both group effect and Time effect, the changes were not significantly different for CRP (P= 0.86) and TNF-α (P=0.69). In contrast, the clinical score and the clinical pulmonary infection score (CPIS) ≥ 6, had 100% specificity for diagnosing VAP. With respect to prognosis, only an unchanged or decreasing TNF-α from day 1 to day 3 was marginally associated with 28-day survival. However, day 1 and day 3 acute physiology and chronic health evaluation II (APACHE II) scores were highly associated with 28-day survival. CONCLUSION: Unlike clinical scoring system including CPIS and APACHE II, TNF-α and CRP levels were not useful as diagnostic or prognostic biomarkers for differentiating between SIRS with VAP etiology and SIRS without infectious etiology.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".