Parameters of the Endocannabinoid System as Novel Biomarkers in Sepsis and Septic Shock
Bibliographic record
Abstract
Sepsis represents a dysregulated immune response to infection, with a continuum of severity progressing to septic shock. This dysregulated response generally follows a pattern by which an initial hyperinflammatory phase is followed by a state of sepsis-associated immunosuppression. Major challenges in improving sepsis care include developing strategies to ensure early and accurate identification and diagnosis of the disease process, improving our ability to predict outcomes and stratify patients, and the need for novel sepsis-specific treatments such as immunomodulation. Biomarkers offer promise with all three of these challenges and are likely also to be the solution to determining a patient's immune status; something that is critical in guiding effective and safe immunomodulatory therapy. Currently available biomarkers used in sepsis lack sensitivity and specificity, among other significant shortcomings. The endocannabinoid system (ECS) is an emerging topic of research with evidence suggesting a ubiquitous presence on both central and peripheral tissues, including an intrinsic link with immune function. This review will first discuss the state of sepsis biomarkers and lack of available treatments, followed by an introduction to the ECS and a discussion of its potential to provide novel biomarkers and treatments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".