Sepsis in the intensive care unit: epidemiology, outcome and host response
Bibliographic record
Abstract
Sepsis is a life-threatening syndrome that arises when a dysbalanced patient response to an infection causes damage to the body’s own organs and tissues. Sepsis is, to date, still a major cause of morbidity and mortality worldwide. A detailed understanding of the pathogenesis of sepsis is likely to aid future individualized sepsis treatment. Early detection of causative pathogens and the development of new molecular techniques for early prognostication can be essential in individualized care for, and treatment of, patients with sepsis. In patients with sepsis, multiple risk factors for unfavorable outcome have been suggested, such as age, gender, premorbid conditions, acute disease conditions and the increased susceptibility to nosocomial infections. In this thesis, we studied the relationship of these host factors with disease severity, host response and outcome. We found that gender and premorbid diabetes were not associated with alterations in the host response, disease severity or outcome in critically ill sepsis patients. However, factors that were of influence included advanced age, dysregulated glucose levels and thrombocytopenia. In addition to this, ICU-acquired infections did not occur more often in patient admitted with sepsis and bear an overall low attributable mortality. The studies described in this thesis are unique in that they integrated detailed clinical and microbiological data with comprehensive host response measurements in one of the largest sepsis cohorts in the world, the MARS cohort.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".