Evaluation of role of Tigecycline among clinically significant multidrug resistant pathogens from a tertiary care hospital
Bibliographic record
Abstract
Background Tigecycline, a glycylcycline antibiotic is a promising option for the treatment of single or multidrug resistant pathogens. The aim of the study was to evaluate the in-vitro Tigecycline susceptibility of various pathogens from clinical samples received at the tertiary care hospitals in South India. Methods The analysis of specimens from patients admitted were carried out in this prospective cross sectional study. The identification and antimicrobial susceptibility testing was performed by semi-automated Vitek 2 systems and Kirby Bauer method. Pattern of data analysis was done by descriptive statistics. Results Among 2574 isolates, 812 isolates were gram positive pathogens and 1762 isolates were gram negative pathogens. Resistance to Tigecycline was more common among gram negative pathogens (18.62%) in comparison to the gram positive pathogens (0.49%). Among 740 Extended Spectrum Beta Lactamases (ESBL) producers such as Klebsiella species & E coli , 629 isolates were susceptible, and 93 isolates were resistant to the tigecycline. All the methicillin resistant Staphylococcus aureus (MRSA) isolates were susceptible to tigecycline. Conclusion Multidrug resistant (MDR) pathogens like Acinetobacter species, and Klebsiella species were found to be highly effective in vitro to tigecycline for elimination of infections caused by both gram positive and gram negative pathogens. The use of combination therapy becomes crucial to prevent the development of Pan Drug resistance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".