Preventive Antibiotics for Infections in Acute Stroke
Bibliographic record
Abstract
OBJECTIVE: To provide a systematic overview and meta-analysis of randomized clinical trials evaluating preventive antibiotics in patients with acute stroke. DATA SOURCES: The MEDLINE (1966-February 2009) and Cochrane databases and reference lists of retrieved articles. STUDY SELECTION: Randomized controlled trials on preventive antibiotic treatment in stroke. For inclusion, at least case fatality or infection rate had to be recorded. DATA EXTRACTION: Each study was scored for methodological key issues and appraised by the Jadad scale. We extracted the data using a predetermined protocol and included all patients who were randomized or who started therapy in an intent-to-treat analysis. DATA SYNTHESIS: We identified 4 randomized clinical trials including 426 patients; 94% had ischemic stroke. Study interventions were fluoroquinolones in 2 and tetracycline or a combination of beta-lactam antibiotic with beta-lactamase inhibitor in 1. Therapy was started within 24 hours of stroke onset. Duration of therapy varied between 3 and 5 days. The methodological quality ranged from 2 to 5 on the Jadad scale, and studies were subject to potential bias. The proportion of patients with infection was significantly smaller in the antibiotic group than in the placebo/control group (32 of 136 [23.5%] vs 53 of 139 [38.1%] patients). The pooled odds ratio for infection was 0.44 (95% confidence interval, 0.23-0.86). Ten of 210 patients (4.8%) in the antibiotic group died, compared with 13 of 216 (6.0%) in the placebo/control group. The pooled odds ratio for mortality was 0.63 (95% confidence interval, 0.22-1.78). No major harm or toxicity was reported. CONCLUSIONS: In adults with acute stroke, preventive antibiotics reduced the risk of infection, but did not reduce mortality. The observed effect warrants evaluation of preventive antibiotics in large stroke trials.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.027 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".