Multiple organ dysfunction syndrome: Exploring the paradigm of complex nonlinear systems
Bibliographic record
Abstract
OBJECTIVES: The objectives of this article are to introduce and explore a novel paradigm based on complex nonlinear systems, and to evaluate its application to critical care research regarding the systemic host response and multiple organ dysfunction syndrome (MODS). DATA SOURCES: Published original work, review articles, scientific abstracts and books, as well as our personal files. STUDY SELECTION: Studies were selected for their relevance to the applications of nonlinear complex systems, to critical care medicine, and to the concepts presented. DATA EXTRACTION: We extracted all applicable data. DATA SYNTHESIS: Following a brief review of MODS, an introduction to complex nonlinear systems is presented, including clear concepts, definitions, and properties. By examining the multiple, nonlinear, interrelated, and variable interactions between the metabolic, neural, endocrine, immune, and inflammatory systems; data regarding interconnected antibody networks; and the redundant, nonlinear, interdependent nature of the inflammatory response, we present the hypothesis that the systemic host response to trauma, shock, or sepsis must be evaluated as a complex nonlinear system. This model provides a new explanation for the failure of trials using various antimediator therapies in the treatment of patients with sepsis and MODS. Understanding the host response as a complex nonlinear system offers innovative means of studying critical care patients, specifically by suggesting a greater focus on systemic properties. We hypothesize that analysis of variability and connectivity of individual variables offer a novel means of evaluating and differentiating the systemic properties of a complex nonlinear system. Current applications of evaluating variability and connectivity are discussed, and insights regarding future research are offered. CONCLUSION: The paradigm offered by the study of complex nonlinear systems suggests new insights to pursue research to evaluate, monitor, and treat patients with MODS.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".