Culture-Negative Septic Shock Compared With Culture-Positive Septic Shock: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To determine the clinical characteristics and outcomes of culture-negative septic shock in comparison with culture-positive septic shock. DESIGN: Retrospective nested cohort study. SETTING: ICUs of 28 academic and community hospitals in three countries between 1997 and 2010. SUBJECTS: Patients with culture-negative septic shock and culture-positive septic shock derived from a trinational (n = 8,670) database of patients with septic shock. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patients with culture-negative septic shock (n = 2,651; 30.6%) and culture-positive septic shock (n = 6,019; 69.4%) were identified. Culture-negative septic shock compared with culture-positive septic shock patients experienced similar ICU survival (58.3% vs 59.5%; p = 0.276) and overall hospital survival (47.3% vs 47.1%; p = 0.976). Severity of illness was similar between culture-negative septic shock and culture-positive septic shock groups ([mean and SD Acute Physiology and Chronic Health Evaluation II, 25.7 ± 8.3 vs 25.7 ± 8.1]; p = 0.723) as were serum lactate levels (3.0 [interquartile range, 1.7-6.1] vs 3.2 mmol/L [interquartile range, 1.8-5.9 mmol/L]; p = 0.366). As delays in the administration of appropriate antimicrobial therapy after the onset of hypotension increased, patients in both groups experienced congruent increases in overall hospital mortality: culture-negative septic shock (odds ratio, 1.56; 95% CI [1.47-1.66]; p < 0.0001) and culture-positive septic shock (odds ratio, 1.65; 95% CI [1.59-1.71]; p < 0.0001). CONCLUSIONS: Patients with culture-negative septic shock behave similarly to those with culture-positive septic shock in nearly all respects; early appropriate antimicrobial therapy appears to improve mortality. Early recognition and eradication of infection is the most obvious effective strategy to improve hospital survival.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".