Personalised immunotherapy in sepsis: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Sepsis, a life-threatening organ dysfunction syndrome occurring in the context of severe infections, remains a major burden on global health with high morbidity and high mortality rates. Despite recent advances in the understanding of its pathophysiology, the treatment of sepsis remains supportive of nature with few interventions specifically designed for treating this complex syndrome. The focus of sepsis trials has increasingly shifted towards targeting excessive inflammation and immunosuppression using immunomodulatory agents. However, it remains uncertain how to identify patients that could benefit from such treatment, whether treatments can be tailored to an individual's immune profile, or at which stage of the disease the intervention should be initiated. In this scoping review, we provide a comprehensive overview of current available literature on immunostimulatory and immunosuppressive therapies against sepsis. METHODS AND ANALYSIS: The aim of this scoping review is to describe and summarise current literature evaluating immunotherapy in adult patients with sepsis. The review will be performed using the framework formulated by Arksey and O'Malley. A comprehensive literature and study collection will be executed by searching PubMed, Embase, Cochrane CENTRAL and ClinicalTrials.gov to identify clinical trials and cohort studies concerning immunotherapy in adult patients with sepsis. Screening will be performed independently and in duplicate by two reviewers who will also independently extract data into prespecified spreadsheets. We will summarise evidence in tabular format with descriptive statistics. The reported evidence will convey knowledge on the types of immunotherapies studied, and currently being studied, in adult patients with sepsis. ETHICS AND DISSEMINATION: Approval from a medical ethics committee is not required. Once completed, the review will be submitted for publication in a peer-reviewed journal. These results will be of value to clinicians and researchers with an interest in advancing sepsis care.
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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 | 0.066 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.070 | 0.011 |
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".