Understanding and Enhancing Sepsis Survivorship. Priorities for Research and Practice
Bibliographic record
Abstract
An estimated 14.1 million patients survive sepsis each year. Many survivors experience poor long-term outcomes, including new or worsened neuropsychological impairment; physical disability; and vulnerability to further health deterioration, including recurrent infection, cardiovascular events, and acute renal failure. However, clinical trials and guidelines have focused on shorter-term survival, so there are few data on promoting longer-term recovery. To address this unmet need, the International Sepsis Forum convened a colloquium in February 2018 titled "Understanding and Enhancing Sepsis Survivorship." The goals were to identify gaps and limitations of current research and shorter- and longer-term priorities for understanding and enhancing sepsis survivorship. Twenty-six experts from eight countries participated. The top short-term priorities identified by nominal group technique culminating in formal voting were to better leverage existing databases for research, develop and disseminate educational resources on postsepsis morbidity, and partner with sepsis survivors to define and achieve research priorities. The top longer-term priorities were to study mechanisms of long-term morbidity through large cohort studies with deep phenotyping, build a harmonized global sepsis registry to facilitate enrollment in cohorts and trials, and complete detailed longitudinal follow-up to characterize the diversity of recovery experiences. This perspective reviews colloquium discussions, the identified priorities, and current initiatives to address them.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".