Deciphering sepsis: transforming diagnosis and treatment through systems immunology
Bibliographic record
Abstract
Sepsis is an abnormal, life-threatening response to infection that leads to (multi-)organ dysfunction and failure. It causes ~20% of deaths worldwide each year, and most deaths related to severe COVID-19 share various molecular features with sepsis. Current treatment approaches (antimicrobials and supportive care) do not address the complexity of sepsis or its mechanistic heterogeneity between and within patients over time. Systems immunology methods, including multiomics (notably RNA sequencing transcriptomics), machine learning, and network biology analysis, have the potential to transform the management paradigm toward precision approaches. Immune dysfunctions evident very early in sepsis drive the development of novel diagnostic gene expression signatures (e.g., cellular reprogramming) that could inform early therapy. Sepsis patients can now be categorized into “endotypes” based on unique immune dysfunction mechanisms corresponding to varying severity and mortality rates, raising the prospect of endotype-specific diagnostics and patient-specific immune-directed therapy. Longitudinal within-patient analyses can also reveal mechanisms (including epigenetics) that drive differential sepsis trajectories over time, enabling the prospect of disease stage-specific therapy during and after hospitalization, including for post-sepsis and long COVID syndromes. Achieving this transformation will require addressing barriers to systems immunology research, including its cost and resource-intensiveness, the relatively low volume of available data, and lack of suitable animal models; it will also require a change in the mindset of healthcare providers toward precision approaches. This should be prioritized in multistakeholder collaborations involving research communities, healthcare providers/systems, patients, and governments to reduce the current high disease burden from sepsis and to mitigate against future pandemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".