Bacteriological profile and antimicrobial resistance pattern among patients with sepsis: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: As the susceptibility pattern of different pathogens varies among different settings, the evaluation of appropriate clinical diagnosis and timely initiation of the empirical antibiotic treatment based on the local susceptibility data is crucial in the management of sepsis. METHODS: A retrospective study was conducted among adult patients with sepsis at a charitable hospital in Mangaluru. The essential details such as patient demographics, culture specimens, organisms, resistance/susceptibility pattern, laboratory data, empirical therapy and clinical outcomes were collected from the medical records. Descriptive statistics were used in analysing the data. RESULTS: A total of 425 patients diagnosed with sepsis during the study period were screened to meet the sample size of 373 positive cultures, among which 367 (91.3%) samples yielded the bacterial isolates, of which 250 (68.1%) and 117 (31.9%) were gram-negative and gram-positive organisms, respectively. The most common gram-negative organisms isolated were K pneumoniae (19.9%), A baumannii (19.6%) and E coli (12.8%); while Coagulase-negative staphylococcus (14.4%) and S aureus (8.4%) were the predominant gram-positive organisms. The isolated pathogens showed a resistance rate of >50% to the most commonly used antibiotics. CONCLUSION: The present study provides information on the prevalence of the most common pathogens and their resistance pattern to different antibiotics, which plays a vital role in the selection and timely initiation of the appropriate empirical antibiotic therapy.
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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 | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".