Indications of a new antibiotic in clinical practice: results of the tigecycline initial use registry
Bibliographic record
Abstract
Tigecycline is the first of a new class of antibiotics named glycylcyclines and it was approved for the treatment of complicated intra-abdominal infections and complicated skin and skin structure infections. Notwithstanding this, tigecycline's pharmacological and microbiological profile which includes multidrug-resistant pathogens encourages physicians' use of the drug in other infections. We analyzed, during the first months after its launch, the tigecycline prescriptions for 113 patients in 12 institutions. Twenty-five patients (22%) received tigecycline for approved indications, and 88 (78%) for "off label" indications (56% with scientific support and 22% with limited or without any scientific support). The most frequent "off label" use was ventilator associated pneumonia (VAP) (63 patients). The etiology of infections was established in 105 patients (93%). MDR-Acinetobacter spp. was the microorganism most frequently isolated (50% of the cases). Overall, attending physicians reported clinical success in 86 of the 113 patients (76%). Our study shows that the "off label" use of tigecycline is frequent, especially in VAP. due to MDR-Acinetobacter spp., where the therapeutic options are limited (eg: colistin). Physicians must evaluate the benefits/risks of using this antibiotic for indications that lack rigorous scientific support.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".