Clinafloxacin versus Piperacillin-Tazobactam in Treatment of Patients with Severe Skin and Soft Tissue Infections
Bibliographic record
Abstract
Patients (n = 409) with severe skin and soft tissue infections (SSTIs) were randomized to receive clinafloxacin or piperacillin-tazobactam (plus optional vancomycin for methicillin-resistant cocci), administered intravenously, with the option to switch to oral medication. Most patients had cellulitis, wound infections, or diabetic foot infections. Staphylococcus aureus, Enterococcus faecalis, and Pseudomonas aeruginosa were the most common baseline pathogens. Fewer baseline pathogens were resistant to clinafloxacin (1.8%) than to piperacillin-tazobactam (6.2%) (P = 0.001). The clinafloxacin and piperacillin-tazobactam groups did not differ significantly in clinical cure rates (68.8 and 65.2%, respectively) or microbiologic eradication rates (61.5 and 57.2%). Clinafloxacin yielded higher eradication rates for all three of the most common pathogenic species, although no differences were statistically significant. Within the power of this study, the overall frequency of adverse events was similar (P = 0.577) in the two treatment groups. Drug-associated adverse events (P = 0.050) and treatment discontinuations (P = 0.052) were marginally more frequent in the clinafloxacin group, primarily due to phototoxicity in outpatients receiving clinafloxacin. Although most cases of phototoxicity were mild to moderate, four cases were reported as severe. In summary, clinafloxacin monotherapy was equivalent in effectiveness to therapy with piperacillin-tazobactam plus optional vancomycin in the treatment of hospitalized patients with severe SSTIs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".