Top 10 research priorities for sepsis research determined by patients, carers and clinicians
Bibliographic record
Abstract
INTRODUCTION: Sepsis is a high burden syndrome associated with increased morbidity and mortality in both the acute and longer-term phases of illness. Multiple treatment uncertainties remain that require resolution through high-quality research. This study aimed to identify the top 10 research priorities for sepsis research in the UK. METHODS: We conducted a priority setting partnership study co-produced by sepsis survivors, carers and clinicians. This included five stages: initiation of steering group formation and confirmation of the scope of the priority setting partnership; identification of clinical uncertainties through an electronic survey; analysis and verification of uncertainties; interim prioritisation to the top 25 ranked questions; and final prioritisation to determine the top 10 research priorities, using the nominal group technique. RESULTS: Our initial survey respondents comprised 447/718 (62.3%) people who had survived sepsis, their friends and family members; 218/718 (30.4%) clinicians; and 53/718 (7.1%) multiple/other roles who identified 53 distinct research uncertainties. Our interim prioritisation survey comprised 429/941 (45.8%) people who had survived sepsis, their friends and family members; 431/941 (46.0%) clinicians; and 73/941 (8.2%) multiple/other roles, with the top 25 ranked summary questions taken forwards for final prioritisation. From these, final workshop participants (n = 27) agreed a top 10 list of research priorities. Improved sepsis diagnosis; characterisation and management of the post-sepsis syndrome; and non-antibiotic treatment of sepsis were the top three priorities. DISCUSSION: We established priorities for sepsis research through a rigorous process of consensus involving sepsis survivors, carers and clinicians. These priorities will support future delivery of meaningful research to improve outcomes from sepsis.
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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 | 0.181 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".