Seven vs Fourteen Days of Antibiotics for Gram-Negative Bloodstream Infection
Bibliographic record
Abstract
Importance: Gram-negative bloodstream infections are a common cause of hospitalization. A 2-week duration of antibiotic therapy has been commonly used, but shorter durations may have similar outcomes. Objectives: To assess whether 7 days of antibiotic therapy was noninferior to 14 days. Data Sources: Starting with a 2022 individual patient data meta-analysis, PubMed, Cochrane Central Register of Controlled Trials, and Web of Science were searched to identify additional eligible randomized clinical trials (RCTs) conducted from May 1, 2022, until November 30, 2024. Study Selection: RCTs involving primarily adults who were hospitalized at the time of Gram-negative bloodstream infection and were allocated to 7 or 14 days of antibiotic therapy. Studies were independently reviewed by 2 investigators. Data Extraction and Synthesis: PRISMA guidelines were followed. Data were extracted by 2 investigators. Any unpublished data were obtained directly from study authors. Risk of bias and certainty of the evidence were assessed in duplicate using the Cochrane Risk of Bias Tool, version 2, and the Grading of Recommendations Assessment, Development and Evaluation approach. Data were pooled by separate random-effects meta-analyses for the intention-to-treat (ITT) and per-protocol (PP) populations. A noninformative prior probability was used for the effect, and an evidence-based weakly informative prior probability was used for heterogeneity. Risk ratios (RRs), 95% credible intervals (CrIs), and probability of noninferiority were calculated using a prespecified upper bound of 1.25 or less. Main Outcomes and Measures: Ninety-day all-cause mortality. Results: Four eligible RCTs contributed 3729 patients in the ITT population (1912 women [51.3%]; median age range, 67-79 years) and 3126 in the PP population. In the ITT analysis, within 90 days, 226 patients (12.8%) receiving 7 days of antibiotics died compared with 253 (13.7%) receiving 14 days, corresponding to an RR for 90-day mortality of 0.91 (95% CrI, 0.69-1.22) and a 97.8% probability of noninferiority. In the PP analysis, the RR was 0.93 (95% CrI, 0.68-1.32), corresponding to a 95.1% probability of noninferiority. Conclusions and Relevance: In this systematic review and meta-analysis of patients with Gram-negative bloodstream infections and adequate source control, 7 days of antibiotic therapy had a high probability of being noninferior to 14 days. These findings support a shorter duration of antibiotic therapy for appropriately selected patients like those in the included RCTs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".