A consensus immune dysregulation framework for sepsis and critical illnesses
Bibliographic record
Abstract
Critical care syndromes such as sepsis, acute respiratory distress syndrome (ARDS) and trauma continue to have unacceptably high morbidity and mortality, with progress limited by the inherent heterogeneity within syndromic illnesses. Although numerous immune endotypes have been proposed for sepsis and critical care, the similarities and differences between these endotypes remain unclear, hindering clinical translation. The SUBSPACE consortium is an international consortium that aims to advance precision medicine in critical care through the sharing of transcriptomic data. Here, evaluating the overlap of existing immune endotypes in sepsis across >7,074 samples from 37 independent cohorts, we developed cell-type-specific gene expression signatures to quantify dysregulation within immune compartments. Myeloid and lymphoid dysregulation were associated with disease severity and mortality across all cohorts. Importantly, this dysregulation was also observed in patients with ARDS, trauma and burns, suggesting a conserved mechanism across various critical illness syndromes. Moreover, analysis of randomized controlled trial data revealed that myeloid and lymphoid dysregulation are associated with differential mortality in patients treated with anakinra in the SAVE-MORE trial (n = 452) and corticosteroids in the VICTAS (n = 89) and VANISH (n = 117) trials, underscoring their prognostic and therapeutic implications. In conclusion, our proposed immunology-based framework for quantifying cellular compartment dysregulation offers a potentially valuable tool for understanding immune dysregulation in critical illness with prognostic and therapeutic significance.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".