Consensus document on controversial issues in the treatment of complicated skin and skin-structure infections
Bibliographic record
Abstract
BACKGROUND: Complicated skin and skin-structure infections (cSSSI), including surgical site infections (SSI), cellulitis, and abscesses, have been extensively studied, but controversial issues still exist. CONTROVERSIAL ISSUES: The aim of this GISIG (Gruppo Italiano di Studio sulle Infezioni Gravi) working group - a panel of multidisciplinary experts - was to define recommendations for the following controversial issues: (1) What is the efficacy of topical negative pressure wound treatment as compared to standard of care in the treatment of severe surgical site infections, i.e., deep infections, caused by Gram-positive microorganisms? (2) Which are the most effective antibiotic therapies in the treatment of cSSSI, including SSI, due to methicillin-resistant staphylococci? Results are presented and discussed. METHODS: A systematic literature search using the MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and www.clinicaltrials.gov databases of randomized controlled trials and/or non-randomized studies was performed. A matrix was created to extract evidence from original studies using the CONSORT method to evaluate randomized clinical trials and the Newcastle-Ottawa Quality Assessment Scale for case-control studies, longitudinal cohorts, and retrospective studies. The GRADE method was used for grading quality of evidence. An analysis of the studies published between 1990 and 2008 is presented and discussed in detail.
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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 | 0.084 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.019 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".