Characterization and Antimicrobial Susceptibility Profiles of Bacteria Isolated from Various Specimens among Mary Begg Health Facilities
Bibliographic record
Abstract
Background: The ever-increasing magnitude of antimicrobial resistance encountered in human pathogens has led to limited treatment options for bacterial infections, consequently reducing antimicrobial efficacy while increasing treatment costs, morbidity, and mortality. In clinical setup, laboratory-based in vitro antimicrobial susceptibility testing is the cornerstone for guiding therapy and enables the monitoring of antimicrobial resistance trends. Aim: To characterize the distribution of bacteria isolated from various specimens and their antibiotic susceptibility profiles in Mary Begg Health facilities. Material & Methods: This was a retrospective, cross-sectional, quantitative, descriptive study that involved the review of 569 laboratory files from three Mary Begg Health facilities from the period of January 2019 to June 2020. A systematic random sampling method was used and SPSS version 21.0 was used for data analysis. Results: The distribution of bacteria based on Gram stain reaction found that most bacteria that were isolated were Gram negative bacilli, 79.5% (171/215). The most common bacterium isolated was Escherichia coli, 46.5% (100/215) followed by Staphylococcus aureus, 12.1% (26/215) and Klebsiella pneumoniae, 17 7.9% (17/215). The study found that E. coli was highly resistant to amoxicillin (95.0%), Ampicillin (90.0%) and Cotrimoxazole (77.0%), respectively. In contrast, E. coli was highly sensitive to Amikacin (96.0%), Ertapenem (91.0%) and Ceftriaxone (80.0%) S. aureus species isolated were sensitive to Gentamicin (65.4%) and Clindamycin (46.2%) but highly resistant to Cotrimoxazole (80.8%). Conclusion: The most frequent isolates were Escherichia coli followed by Staphylococcus aureus and majority of them were from urine specimens. Key words: Antimicrobial, Resistant, Antimicrobial Resistance, Antimicrobial susceptibility testing, Mary Begg Health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".