EARLY ELEVATION IN RANDOM PLASMA IL-6 AFTER SEVERE INJURY IS ASSOCIATED WITH DEVELOPMENT OF ORGAN FAILURE
Bibliographic record
Abstract
Excessive proinflammatory activation after trauma plays a role in late morbidity and mortality, including the development of multiple organ dysfunction syndrome (MODS). To date, identification of patients at risk has been challenging. Results from animal and human studies suggest that circulating interleukin 6 (IL-6) may serve as a biomarker for excessive inflammation. The purpose of this analysis was to determine the association of IL-6 with outcome in a multicenter developmental cohort and in a single-center validation cohort. Severely injured patients with shock caused by hemorrhage were evaluated within a multicenter developmental cohort (n = 79). All had blood drawn within 12 h of injury. Plasma IL-6 was determined by multiplex proteomic analysis. Clinical and outcome data were prospectively obtained. Within this developmental cohort, a plasma IL-6 level was determined for the subsequent development of MODS by developing a receiver operating curve and defining the optimal IL-6 level using the Youden Index. This IL-6 level was then evaluated within a separate validation cohort (n = 56). A receiver operating curve was generated for IL-6 and MODS development, with an IL-6 level of 350 pg/mL having the highest sensitivity and specificity within the developmental cohort. IL-6 was associated with MODS after adjusting for Acute Physiology and Chronic Health Evaluation, Injury Severity Score, male sex, and blood transfusions with an odds ratio of 3.9 (95% confidence interval, 1.33 - 11.19). An IL-6 level greater than 350 pg/mL within the validation cohort was associated with an increase in MODS score, MODS development, ventilator days, intensive care unit length of stay, and hospital length of stay. However, this IL-6 level was not associated with either the development of nosocomial infection or mortality. Elevation in plasma IL-6 seems to correlate with a poor prognosis. This measurement may be useful as a biomarker for prognosis and serve to identify patients at higher risk of adverse outcome that would benefit from novel therapeutic interventions.
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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 | 0.003 |
| 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 | 0.001 |
| 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".