Bibliographic record
Abstract
Sepsis, the most common cause of admission to an intensive care unit (ICU), has had an increased incidence and prevalence over the last years with a simultaneous decrease in its short-term mortality. Sepsis survivors are more frequently discharged from hospital and often experience long-term outcomes such as late mortality, immune dysfunction, secondary infections, impaired quality of life, and unplanned readmissions. Early recognition and management of sepsis have challenged emergency care and critical care physicians and nurses. New sepsis definitions were produced and the Surviving Sepsis Campaign (SSC) 2016 was updated recently. Although hospital readmissions after sepsis are common, associated risk factors and how to manage patients who survive an episode of sepsis still need clarification. The immune dysfunction caused by sepsis/septic shock is complex, persistent, affects inflammatory and anti-inflammatory systems, and might be associated with long-term outcomes of sepsis. Several randomized controlled trials (RCT) that analyzed new (and old) interventions in sepsis/septic shock are discussed in this review in parallel with the SSC 2016 recommendations and other guidelines when relevant. RCTs addressing incidence, treatment, and prevention of important sepsis-associated organ dysfunction such as the acute respiratory distress syndrome, acute kidney injury, and brain dysfunction are highlighted. Finally, we briefly discuss the need for novel targets, predictive biomarkers, and new designs of RCTs in 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".