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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".