Efficacy of telavancin in patients with specific types of complicated skin and skin structure infections
Bibliographic record
Abstract
BACKGROUND: Telavancin is approved in the USA and Canada for the treatment of Gram-positive complicated skin and skin structure infections (cSSSIs) based on the results of the Phase 3 Assessment of TeLAvancin in complicated Skin and skin structure infections (ATLAS) trials, which demonstrated non-inferiority of telavancin to vancomycin. METHODS: We conducted a post hoc analysis of the ATLAS studies (ClinicalTrials.gov identifiers NCT00091819 and NCT00107978) to explore the efficacy of telavancin in patients with various types of cSSSIs. RESULTS: A total of 1794 patients were included in this analysis; 1434 patients were clinically evaluable (CE) and 563 of these had methicillin-resistant Staphylococcus aureus (MRSA). Among CE patients with major abscesses (n = 619), cure rates were 91% for telavancin and 90% for vancomycin (95% CI for the difference -3.6 to 5.7). In patients with infective cellulitis (n = 519), cure was achieved in 87% and 88% of telavancin- and vancomycin-treated patients, respectively (95% CI for the difference -6.2 to 5.2). Cure rates in patients with wound infections were 85% in the telavancin group and 86% in the vancomycin group (95% CI for the difference -10.5 to 9.0). Cure rates for each type of cSSSI in patients infected with MRSA were also similar between the two treatment arms. Among CE patients infected with Panton-Valentine leucocidin (PVL)-positive MRSA (n = 447), cure rates were 93% for telavancin and 90% for vancomycin (95% CI for the difference -2.2 to 8.2). CONCLUSIONS: Cure rates were similar for telavancin and vancomycin in patients with different types of cSSSIs, including infections caused by MRSA and PVL-positive strains of MRSA.
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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 | 0.003 |
| 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 | 0.000 | 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".