A systematic methodological evaluation of sepsis guidelines: Protocol for quality assessment and consistency of recommendations
Bibliographic record
Abstract
BACKGROUND: Sepsis is a leading cause of mortality worldwide, characterized by a dysregulated host response to infection. Despite the development of multiple clinical practice guidelines (CPGs) to standardize sepsis management, substantial variability exists in methodological quality and key clinical recommendations. This inconsistency complicates guideline implementation and potentially affects patient outcomes. The proposed systematic methodological review aims to evaluate the quality and consistency of sepsis guidelines to identify areas for improvement and provide actionable insights for guideline developers. METHODS: This protocol outlines a systematic methodological review of sepsis CPGs published over the last two decades (2004-2025). A comprehensive search strategy will be conducted across PubMed, EMBASE, the Cochrane Library, and the official websites of professional societies to identify relevant guidelines. The inclusion criteria are CPGs targeting adult sepsis management published by recognized medical or governmental organizations with detailed methodological descriptions. We will use the Appraisal of Guidelines for Research and Evaluation II instrument to assess methodological quality across six domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence. Data extraction will focus on key clinical recommendations, including fluid resuscitation, antimicrobial therapy, vasopressor and inotrope use, corticosteroids, source control, blood glucose management, hemodynamic management, and mechanical ventilation management. The consistency of the recommendations will be analyzed, and trends in guideline quality over time will be evaluated. Artificial intelligence (AI) tools will be evaluated for data extraction processes in systematic reviews to determine their capacity for efficiency and accuracy in extracting data compared to human-driven methods. CONCLUSION: By systematically appraising the quality and consistency of sepsis guidelines, this review aims to address the existing gaps and discrepancies in guideline development and application. These findings will provide valuable insights into the evolution of sepsis guideline quality, highlight areas for improvement, and support the development of more robust evidence-based recommendations. These results will inform clinicians and guideline developers, ultimately enhancing the standardization and effectiveness of sepsis management worldwide. Integrating AI into the review process represents a novel methodological advancement that streamlines data extraction and analysis.
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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 | 0.340 | 0.482 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.020 | 0.028 |
| Bibliometrics | 0.022 | 0.028 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.050 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".