Differential Network Testing Reveals Diverging Dynamics of Organ System Interactions for Survivors and Non-survivors in Intensive Care Medicine
Bibliographic record
Abstract
Statistical network analyses have become popular in many scientific disciplines, where an important task is to test for differences between two networks. We describe an overall framework for differential network testing procedures that vary regarding (1) the network estimation method, typically based on specific concepts of association, and (2) the network characteristic employed to measure the difference. Using permutation-based tests, our approach is general and applicable to various overall, node-specific or edge-specific network difference characteristics. The methods are implemented in our freely available R software package DNT, along with an R Shiny application. In a study in intensive care medicine, we compare networks based on parameters representing main organ systems to evaluate the prognosis of critically ill patients in the intensive care unit (ICU), using data from the surgical ICU of the University Medical Centre Mannheim, Germany. We specifically consider both cross-sectional comparisons between a non-survivor and a survivor group and longitudinal comparisons at two clinically relevant time points during the ICU stay: first, at admission, and second, at an event stage prior to death in non-survivors or a matching time point in survivors. The non-survivor and the survivor networks do not significantly differ at the admission stage. However, the organ system interactions of the survivors then stabilize at the event stage, revealing significantly more network edges, whereas those of the non-survivors do not. In particular, the liver appears to play a central role for the observed increased connectivity in the survivor network at the event stage.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".