Mortality in sepsis versus non-sepsis induced acute lung injury
Bibliographic record
Abstract
INTRODUCTION: Sepsis-induced acute lung injury (ALI) has been reported to have a higher case fatality rate than other causes of ALI. However, differences in the severity of illness in septic vs. non-septic ALI patients might explain this finding. METHODS: 520 patients enrolled in the Improving Care of ALI Patients Study (ICAP) were prospectively characterized as having sepsis or non sepsis-induced ALI. Biologically plausible risk factors for in-hospital death were considered in multiple logistic regression models to evaluate the independent association of sepsis vs. non-sepsis ALI risk factors with mortality. RESULTS: Patients with sepsis-induced ALI had greater illness severity and organ dysfunction (APACHE II and SOFA scores) at ALI diagnosis and higher crude in-hospital mortality rates compared with non-sepsis ALI patients. Patients with sepsis-induced ALI received similar tidal volumes, but higher levels of positive end expiratory pressure, and had a more positive net fluid balance in the first week after ALI diagnosis. In multivariable analysis, the following variables (odds ratio, 95% confidence interval) were significantly associated with hospital mortality: age (1.04, 1.02 to 1.05), admission to a medical intensive care unit (ICU) (2.76, 1.42 to 5.36), ICU length of stay prior to ALI diagnosis (1.15, 1.03 to 1.29), APACHE II (1.05, 1.02 to 1.08), SOFA at ALI diagnosis (1.17, 1.09 to 1.25), Lung Injury Score (2.33, 1.74 to 3.12) and net fluid balance in liters in the first week after ALI diagnosis (1.06, 1.03 to 1.09). Sepsis did not have a significant, independent association with mortality (1.02, 0.59 to 1.76). CONCLUSIONS: Greater severity of illness contributes to the higher case fatality rate observed in sepsis-induced ALI. Sepsis was not independently associated with mortality in our study.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".