Tigecycline Treatment of Urinary Tract Infection and Prostatitis: Case Report and Literature Review
Bibliographic record
Abstract
1suggested that the clinical and microbiological outcomes of tigecycline monotherapy were similar to those of empiric antibiotic regimens in the treatment of complicated skin and skin structure infections, intra-abdominal infections, and community-acquired pneumonia and other infections caused by methicillin-resistant Staphylococcus aureus (MRSA) or vancomycin-resistant Enterococcus (VRE). However, the incidence of adverse events, particularly nausea, was significantly higher with tigecycline than with the comparators. 1 In September 2010, the US Food and Drug Administration issued a safety communication suggesting an increased risk of death with tigecycline relative to other antibiotics used to treat similar infections. 2 The risk was greatest for patients with hospital-acquired pneumonia, especially ventilator-associated pneumonia. The clinical use of tigecycline therapy is generally reserved for treatment of multidrug-resistant (MDR) organ isms. There are very limited data regarding its use in the treat ment of urinary tract infections, as urinary excretion is a minor route of elimination for this drug. Even with no clinical trials, the drug has been suggested as an alternative for the treatment of complicated and nosocomial or catheter-related urinary tract infections caused by MDR Enterobacteriaceae, Acinetobacter baumannii, and VRE. 3
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".