What Is the Impact of Anti-Enterococcal Empirical Therapy on Survival of Patients With Enterococcal Bloodstream Infections?
Bibliographic record
Abstract
BACKGROUND: The impact of early administration of active anti-enterococcal empirical therapy (EET) on outcomes of patients with enterococcal bloodstream infections (EBSIs) is unclear. We compared the outcome of patients with EBSIs receiving or not empirical therapy targeting Enterococcus spp. METHODS: A retrospective multicenter study enrolling all hospitalized patients with monomicrobial EBSIs during 2011-2019. The exposure variable was considered receiving EET defined as administration of antibiotic(s) active in vitro against Enterococcus spp isolated from index blood cultures (BCs) ≤48 hours from collection. The primary outcome was 14- and 30-day all-cause mortality from index BC. The impact of EET on mortality was assessed by Kaplan-Meier curves and multivariable Cox regression after adjustment with inverse probability treatment weighting (IPTW). Post hoc analysis explored the effect of EET given ≤24 hours from EBSI onset. RESULTS: Overall, 758 patients (male, 62%; median age, 71 years) had EBSIs and 342 received EET; 14- and 30-day mortality was 24% and 42%, respectively. After IPTW adjustment, a higher number of comorbidities and EBSIs without an identified source but not EET were independent predictors of 14- and 30-day mortality. No significant mortality risk reduction was associated with EET in the real or the IPTW-adjusted analysis. In the subgroup analysis, EET reduced 14-day mortality only in patients with vancomycin-resistant EBSIs. Even among 237 (31%) patients receiving EET <24 hours from EBSI onset, EET did not affect survival. CONCLUSIONS: Early administration of EET may not have a prognostic impact in patients with EBSIs. Delivering EET might affect short-term survival only in patients with vancomycin-resistant EBSIs.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".