Current Trends of Resistance Among Gram Negative and Gram Positive Bacteria Isolated from Tertiary Care Hospital
Bibliographic record
Abstract
Introduction: Nosocomial infections (NIs) following antimicrobial resistance becomes serious public health concern across the world. It affects the length of hospital stay and cost burden on the health care system. Rapid increase in resistance against commonly used antibiotics results in emergence of multi-drug resistant organisms. Material and Methods: This retrospective study was done at CMH Sialkot over the period of six months. All types of clinical samples were included and were processed according to standard microbiological procedures. Organisms were identified by using different biochemical tests. AST were performed by Kirby-Bauer disc diffusion method according to CLSI guidelines. Results: Total 402 organisms were isolated among which 57% were gram positive while 43% were Gram negative bacteria. MRSA (31%) was most prevalent in Gram positive bacteria while E.coli(50%) was found to be most common in Gram negative bacteria. Gram-positive bacteria showed maximum resistance against Fluoroquinolones, while Co-trimoxazole and Cephalosporins were least susceptible against Gram-negative bacteria. Conclusion: Nosocomial infections Because of their importance, antibiotic usage policies should be taken and implemented on a priority basis in the agenda of the infection control committees of hospitals.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".