Shorter versus longer-duration antibiotic treatments for immunocompetent patients with bloodstream infections: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The optimal duration of antibiotic treatment for bloodstream infections remains uncertain. This study aimed to compare the efficacy and safety of shorter versus longer duration antibiotic treatment for immunocompetent patients with bloodstream infections. Methods: We searched Medline, Embase, and Cochrane Central Register of Controlled Trials without language restrictions for randomized controlled trials (RCTs) published between database inception and 25 November 2024, comparing shorter-duration antibiotic treatments with longer-duration antibiotic treatments in patients with bloodstream infections with a minimum difference of 3 days in duration of therapy. We employed random-effects meta-analyses to summarize the evidence. We used the mean difference (MD) with 95% confidence intervals (CIs) for continuous outcomes. For dichotomous outcomes, we used the odds ratios (ORs) or risk ratios (RRs) with 95% CIs. We used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework to assess the certainty of evidence. This study is registered with Open Science Framework (https://osf.io/egs8q). Findings: Eleven RCTs published between 2006 and 2025, involving a total of 5505 participants, proved eligible. There are probably little or no differences in mortality (RR 0.91, 95% CI 0.79-1.05; moderate certainty), treatment failure (RR 1.08, 95% CI 0.69-1.68; moderate certainty), and relapse rates (RR 1.15, 95% CI 0.82-1.63; moderate certainty) between shorter- and longer-duration antibiotics. Compared with longer-duration antibiotics, shorter-duration antibiotics do not increase hospital readmission (RR 0.91, 95% CI 0.75-1.1; high certainty), but reduce the length of hospital stay (MD -3.04, 95% CI -3.9 to -2.18; high certainty). With very low certainty evidence, we are uncertain whether shorter-duration antibiotics decrease any adverse events (RR 1.0, 95% CI 0.76-1.32) and serious adverse events (RR 0.67, 95% CI 0.39-1.14) compared with longer-duration antibiotics. Interpretation: Shorter- and longer-duration antibiotics show similar efficacy for bloodstream infections, with shorter courses reducing hospital stay. Consistent effects were observed across age groups and bacterial types, but cautious interpretation is needed due to limited subgroup data. Funding: None.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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".