Patient and Organizational Factors Associated With Delays in Antimicrobial Therapy for Septic Shock*
Bibliographic record
Abstract
OBJECTIVES: To identify clinical and organizational factors associated with delays in antimicrobial therapy for septic shock. DESIGN: In a retrospective cohort of critically ill patients with septic shock. SETTING: Twenty-four ICUs. PATIENTS: A total of 6,720 patients with septic shock. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Higher Acute Physiology Score (+24 min per 5 Acute Physiology Score points; p < 0.0001); older age (+16 min per 10 yr; p < 0.0001); presence of comorbidities (+35 min; p < 0.0001); hospital length of stay before hypotension: less than 3 days (+50 min; p < 0.0001), between 3 and 7 days (+121 min; p < 0.0001), and longer than 7 days (+130 min; p < 0.0001); and a diagnosis of pneumonia (+45 min; p < 0.01) were associated with longer times to antimicrobial therapy. Two variables were associated with shorter times to antimicrobial therapy: community-acquired infections (-53 min; p < 0.001) and higher temperature (-15 min per 1°C; p < 0.0001). After adjusting for confounders, admissions to academic hospitals (+52 min; p< 0.05), and transfers from medical wards (medical vs surgical ward admission; +39 min; p < 0.05) had longer times to antimicrobial therapy. Admissions from the emergency department (emergency department vs surgical ward admission, -47 min; p< 0.001) had shorter times to antimicrobial therapy. CONCLUSIONS: We identified clinical and organizational factors that can serve as evidence-based targets for future quality-improvement initiatives on antimicrobial timing. The observation that academic hospitals are more likely to delay antimicrobials should be further explored in future trials.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".