Severe bloodstream infections: A population-based assessment*
Bibliographic record
Abstract
OBJECTIVE: Although bloodstream infection commonly results in critical illness, population-based studies of the epidemiology of severe bloodstream infection are lacking. We sought to define the incidence and microbiology of severe bloodstream infection (bloodstream infection associated with intensive care unit admission within 48 hrs) and assess risk factors for acquisition and death. DESIGN: Population-based surveillance cohort. SETTING: Multidisciplinary and cardiovascular surgical intensive care units. PATIENTS: All adults with severe bloodstream infection in the Calgary Health Region (population approximately 1 million) during 2000-2002. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Three hundred forty patients had 342 episodes of severe bloodstream infection (15.7 per 100,000 population/year). Several demographic and chronic conditions were significant risk factors for acquiring severe bloodstream infection (relative risk, 95% confidence interval) including age > or =65 yrs (7.0, 5.6-8.7), male gender (1.3, 1.1-1.6), urban residence (2.4, 1.2-5.6), hemodialysis (208.7, 142.9-296.3), diabetes mellitus (5.9, 4.4-7.8), alcoholism (5.6, 3.8-8.0), cancer (7.5, 5.3-10.3), and lung disease (3.8, 2.6-5.4). The most common etiologies were Staphylococcus aureus, Escherichia coli, and Streptococcus pneumoniae (3.0, 3.0, and 1.9 per 100,000/year, respectively). The case-fatality rate was 142 of 340 (42%) for an annual mortality rate of 6.5 per 100,000. Increased Acute Physiology and Chronic Health Evaluation II score (odds ratio, 1.1 per point; 95% confidence interval, 1.1-1.2) and presence of a comorbidity (odds ratio, 2.5; 95% confidence interval, 1.4-4.3) were significant independent predictors of death. CONCLUSIONS: Bloodstream infections are commonly severe enough to require management in an intensive care unit and are associated with a high mortality rate. Identification of risk factors for severe bloodstream infection may allow targeting of preventive efforts to individuals at greatest potential benefit.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".