Sepsis: Diagnostic and Therapeutic Challenges
Bibliographic record
Abstract
Intensive and critical care medicine has gone through unprecedented development over the last few decades. According to recent surveys, we now treat severalfold more critically ill patients in intensive care units (ICU) worldwide as compared to 10 years ago [1]. One of the most challenging tasks that intensive care specialists face is the treatment of serious infection-related multiple organ dysfunction, termed “sepsis” and “septic shock.” Sepsis has become a serious health economic issue around the world, with more patients dying due to sepsis related complications than breast and colorectal cancer together. According to recent data from the United States and Germany, sepsis is the single most expensive reason for hospitalization [2–4]. Large retrospective and prospective studies indicate that mortality of septic shock can still be as high as 45–55% and is associated with a 2- to 3-fold longer ICU and hospital stay [4, 5]. Accordingly, sepsis has become a serious health economic issue; hence, research of new frontiers in the diagnosis and treatment of sepsis has been a top priority in intensive care medicine.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".