Sepsis in European intensive care units: Results of the SOAP study*
Bibliographic record
Abstract
Recent years have seen severalstudies providing importantnational and international ep-idemiologic data on the fre-quency, associated factors, and even costs of sepsis (1–7). Angus and coworkers (1) analyzed 6 million hospital discharge records from seven states in the United States and estimated that 751,000 cases of severe sepsis occur annually in the United States, with a mortality rate of 28.6 % and leading to average costs per case of $22,100. Using the National Hos-pital Discharge Survey database, Martin et al. (2) identified 10,319,418 cases of sepsis from an estimated 750 million hos-pitalizations in the United States over a 22-yr period, with an increase in fre-quency from 82.7 cases per 100,000 pop-ulation in 1979 to 240.4 cases per 100,000 population in 2000. Alberti and colleagues (3) examined 14,364 patients in six European countries and Canada with 4,500 documented infectious epi-sodes and reported a hospital mortality rate of 16.9 % for noninfected patients and 53.6 % for patients who had repeated courses of infection while in the intensive care unit (ICU). The European Prevalence of Infection in intensive Care (EPIC) study (8), now 10 yrs old, demonstrated how interna-tional collaboration can succeed in pro-viding valuable information regarding disease prevalence and demographics of critically ill patients. In that prevalence Objective: To better define the incidence of sepsis and the characteristics of critically ill patients in European intensive care units. Design: Cohort, multiple-center, observational study. Setting: One hundred and ninety-eight intensive care units in
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".