Antimicrobial Activity of Gram Negative Bacteria in Blood Cultures: A Study with Blood Agar in ICU of LGH, Lahore
Bibliographic record
Abstract
Aim: To evaluate gram-negative rods' prevalence and antibiotic resistance, which are responsible for bloodstream infections in ICU patients. Study design: Cross-sectional study Place and duration of study: Lahore General Hospital, Lahore from 1stJuly 2022 to 31th December 2022. Methodology: Total 200 patients in ICU suspected to have bloodstream infection were enrolled in this study. Blood samples were cultured on Blood agar and then subjected to antibiotic susceptibility testing by the disk diffusion method.Statistical analysis was performed by using SPSS Software. Results: 135 were positive for Gram-negative bacterial strains and 13 show gram positive strain with 25 showing mixed growth and 27 showing no growth .In all positive cases for different bacterial isolates resistance for amikacin was 60.8%, cefepime 66.8%, ceftriaxone 79%, ceftazidime 75.6%, cotrimoxazole 57.4%, ciprofloxacin 61.4%, gentamycin 60.8%, levofloxacin 59.4%, imipenem 58.7%, meropenem 65.5%, doxycycline 51.3%, tazobactam 71.6%, and piperacillin + tazobactam74.3%. Overall gram-negative bacterial isolates exhibited a high resistance rate for cefotaxime at 79% and the lowest resistance rate for doxycyclineat 51.3%. Out of 148 positive cases, 70(35%) were male and 78(39%) were females. Most positive cases were reported in the age group of 16-25 years (33.3%). Conclusion: All the positive samples showed gram-negative rods and gram positive on gram staining. Klebsiella 37(18.5%) was the most prevalent pathogen found in blood samples of ICU patients. Keywords: Prevalence, antibiotic resistance, bloodstream infections, gram-negative rods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".