Efficacy and Safety of Iclaprim for the Treatment of Skin Structures and Soft Tissue Infections: A Methodological Framework
Bibliographic record
Abstract
Background: Skin and soft tissue infections (SSTIs) are among the most common infections worldwide. They manifest in a variety of forms, such as erysipelas, cellulitis, and necrotizing fasciitis. Antibiotics are the significant method for clinical treatment of SSTIs. This study reported a methodology framework to determine the efficacy and safety of iclaprim in treatment of SSTIs. Methods: We will search the PubMed, EMbase, CNKI, WanFang Data, VIP, and ClinicalTrials.gov from their inception to June 2022 for randomized controlled trials and cohort studies on iclaprim with SSTIs. Two authors will independently screen the eligible studies, assess the quality of the included papers, and extract the required information. Randomized controlled trials will be assessed using the Cochrane risk-of-bias tool. The Newcastle–Ottawa Scale will be used to evaluate observational studies. The quality of the evidence will be evaluated using the Grading of Recommendations Assessment Development and Evaluation system. RevMan 5.3 will be used for the data synthesis and quantitative analysis. Results and Discussions: This study will provide the clinicians with more high-quality evidence to choose iclaprim for patients with SSTIs. Ethics and Dissemination: This systematic review and meta-analysis will be based on published data, so ethical approval is not necessary. The results of this meta-analysis will be published in a peer-reviewed journal.
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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 | 0.323 | 0.426 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.029 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".