Prolonged infusion with β-lactam antibiotics for treatment of infection caused by non-susceptible bacteria: a study protocol for a systemic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Prolonged infusion with β-lactam antibiotics should theoretically produce a better clinical efficacy than intermittent infusion in severe infection and infection caused by non-susceptible micro-organisms. The efficacy of prolonged infusion in severe infection has been well illustrated recently, but is still confusing in non-susceptible microbial infection. The objective of this meta-analysis is to determine the clinical effects of prolonged infusion with β-lactams for patients infected by microbes non-susceptible to the given drug. METHODS AND ANALYSIS: Literature searches will be performed with Medline, the Cochrane database, EMBASE database, Cumulative Index to Nursing and Allied Health Literature database, the Chinese National Knowledge Infrastructure and Wanfang database. Two reviewers will screen and select studies according to a priori defined eligibility criteria, and then the data from the included studies will be extracted. The quality will be evaluated based on a modified Jadad score and the Newcastle-Ottawa system for randomised controlled trials and observational studies, respectively. Data synthesis will be performed with Review Manager 5.3 software. Sensitivity analysis and publication bias will also be investigated. ETHICS AND DISSEMINATION: No ethics approval is required. The full article will be published in a peer-reviewed journal and presented at international conferences. PROSPERO REGISTRATION NUMBER: CRD42018105111.
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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 | 0.057 | 0.059 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.025 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.058 | 0.007 |
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".