376. 7 versus 14 days of Antibiotic Treatment for Patients with Bloodstream Infections
Bibliographic record
Abstract
Abstract Background Bloodstream infections are associated with substantial morbidity and mortality. Early, appropriate antibiotic therapy is important, but optimal duration of treatment is uncertain. Methods In a multicenter, non-inferiority trial, we randomly assigned hospitalized (ward and intensive care unit [ICU]) patients with bloodstream infection to receive 7- versus 14-day antibiotic treatment. Antibiotic selection, dosing and route were at the discretion of the treating team. We excluded patients with severe immune-suppression, foci requiring prolonged treatment, single cultures with potential contaminants, or cultures yielding Staphylococcus aureus. The primary outcome was 90-day mortality, with a 4% absolute non-inferiority margin. Results Across 74 hospitals in 7 countries, 3608 patients were randomized and included in the intention-to-treat analysis (1,814 to 7-day and 1,794 to 14-day antibiotic treatment). At enrolment, 55% of patients were in the ICU and 45% on hospital wards. Infections were community-onset (75.4%), hospital-acquired (13.4%) and ICU-acquired (11.2%). Bacteremia sources were most commonly from the urinary tract (42.2%), abdomen (18.8%), lung (13.0%), vascular catheters (6.3%), and skin or soft tissue (5.2%). The primary outcome of 90-day mortality occurred in 261 (14.5%) patients receiving 7-day and in 286 (16.1%) receiving 14-day treatment (absolute difference -1.6% [95.7% confidence interval -4.0 to 0.8]), demonstrating non-inferiority of shorter duration treatment. A per-protocol analysis also demonstrated non-inferiority (-2.0% [95% confidence interval -4.5 to 0.6]). These findings were generally consistent across secondary clinical outcomes, and pre-specified patient, pathogen and syndrome subgroups. Conclusion Treating hospitalized patients with bloodstream infection with antibiotics for 7 days is non-inferior to treating for 14 days. (Funded by the Canadian Institutes of Health Research, Health Research Council of New Zealand, Australian National Medical Research Council, Physicians Services Incorporated Ontario and Ontario Ministry of Health and Long-term Care Innovation Fund; ClinicalTrials.gov NCT03005145) Disclosures All Authors: No reported disclosures
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".