Factors associated with systemic inflammatory response syndrome negativity at the early stage of sepsis among nonsurviving sepsis patients in intensive care unit
Bibliographic record
Abstract
Abstract Objectives This study aims to investigate the factors associated with systemic inflammatory response syndrome (SIRS) negativity during the early stage of sepsis in deceased septic patients. Methods Adult septic patients were included from the Medical Information Mart for Intensive Care IV (MIMIC‐IV) database between 2008 and 2019. Patients who did not survive after 28 days were assigned to the SIRS‐negative or SIRS‐positive group according to whether the SIRS score was less than two points within 24 h of intensive care unit admission. Logistic regression and a restricted cubic spline model were used to analyze factors and dose–response relationships. Results A total of 53,150 patients were screened in the MIMIC‐IV database, and 2706 sepsis nonsurvivors were ultimately included, 101 of whom were negative for SIRS. There were significant differences in sequential organ failure assessment (SOFA) scores between groups (8.18 ± 3.58 vs. 9.75 ± 4.28, p < 0.001). Logistic regression analysis indicated that lactate (odds ratio [OR] = 0.75 [95% CI = 0.62–0.90], p = 0.002), SOFA score (OR = 0.93 [95% CI = 0.87–1.00], p = 0.046), and age (OR = 1.04 [95% CI = 0.88–1.15], p = 0.012) were independent factors related to SIRS negativity in septic patients. Analysis with a restricted cubic spline model showed that the OR of SIRS negativity continued to increase with age, particularly for those over 80 years old ( p for nonlinearity = 0.024). The OR of SIRS negativity was more than 1 when the SOFA score was <4 ( p for nonlinearity = 0.149) and when the lactate was <1 ( p for nonlinearity = 0.014). Conclusions For sepsis patients with poor prognoses, elderly individuals are more likely to be SIRS negative when they have mild organ dysfunction damage or mild tissue hypoperfusion in the early stage of sepsis. This warranted an opportunity to provide early diagnosis for elderly population with negative SIRS score in order to prevent poor outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".