Antibiotic de-escalation in patients with pneumonia in the intensive care unit: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES OF THE REVIEW: Antibiotic de-escalation is part of an antibiotic stewardship strategy to achieve adequate therapy for infections while avoiding the prolonged use of broad-spectrum antibiotics. However, there is a paucity of clinical evidence on the clinical impact of this strategy in pneumonia patients in the intensive care unit (ICU). This review aimed to evaluate the impact of antibiotic de-escalation therapy for adult patients diagnosed with pneumonia in the ICU. METHODS USED TO CONDUCT THE REVIEW: This review was conducted in accordance with the Meta-analysis of Observational Studies in Epidemiology (MOOSE) recommendation. Electronic databases including MEDLINE, CINAHL, PubMed, Embase, Cochrane Databases and Cochrane Central Register of Controlled Trials were searched up to March 2017 for relevant trials. The methodological quality of included trials was assessed by using a modified version of the Newcastle-Ottawa Quality Assessment Scale for Case-Control and Cohort Studies. A meta-analysis was conducted using the random-effect model to combine the rate of mortality and length of stay outcomes. FINDINGS OF THE REVIEW: Nine observational trials involving 2128 patients were considered eligible for inclusion. Although based on low quality evidence, there was a statistically significant difference in favour of the impact of de-escalation on hospital stay but not mortality (MD -5.96 days; 95% CI -8.39 to -3.52). INTERPRETATIONS AND IMPLICATIONS OF THE FINDINGS: This review highlights the need for more rigorous studies to be carried out before a firm conclusion on the benefit of de-escalation therapy is supported.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".