Bibliographic record
Abstract
OBJECTIVE: To review the pharmacokinetic and clinical evidence for the use of once-daily cefazolin and probenecid in the treatment of skin and soft tissue infections (SSTI). DATA SOURCES: MEDLINE (1966-July 2003), EMBASE (1980-July 2003), and PubMed (1966-July 2003) databases for English language, human reports were searched. Search terms included cefazolin, probenecid, cellulitis, and soft tissue infections. STUDY SELECTION AND DATA EXTRACTION: Studies that described pharmacokinetic and clinical outcomes that evaluated the use of cefazolin in conjunction with probenecid for SSTI were included. All studies were evaluated independently by both authors. For pharmacokinetic studies, the effect of probenecid on the pharmacokinetics of cefazolin was evaluated. For clinical trials, efficacy and safety endpoints were evaluated. For efficacy endpoints, definition of cure was used as defined by each trial. DATA SYNTHESIS: In all 3 pharmacokinetic studies identified, the addition of probenecid to cefazolin therapy prolonged the half-life and increased serum concentrations of cefazolin. This process allowed serum concentrations to be above the minimal inhibitory concentrations (MIC) for the most likely skin pathogens (Staphylococcus aureus, beta-hemolytic streptococci) at the end of the dosing interval. In the first of 2 clinical trials, 7 (7%) of 96 patients receiving intravenous ceftriaxone 2 g and oral probenecid 1 g daily were reported to fail therapy compared with 8 (8%) of 98 patients receiving intravenous cefazolin 2 g and oral probenecid 1 g daily. In the second clinical trial, clinical success was reported in 51 (86%) of 59 patients receiving the same doses of cefazolin and probenecid as above compared with 55 (96%) of 57 patients receiving intravenous ceftriaxone 1 g and oral placebo daily. CONCLUSIONS: Limited pharmacokinetic and clinical data suggest that intravenous cefazolin 2 g and oral probenecid 1 g daily is an effective regimen in the treatment of SSTI.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".