Carbapenemase typing and resistance profile of Enterobacteriaceae with reduced sensitivity to carbapenems in a Middle Eastern tertiary care center
Bibliographic record
Abstract
Objective: nowadays resistant bacteria represent worldwide a public health problem leading in some cases to a stalemate without any possible treatment. Therefore early detection and identification of carbapenemase producing gram-negative bacteria (GNB) is of crucial importance. Consequently we conducted a study in a tertiary care hospital to analyze the resistance phenotype of the carbapenem resistant GNB (CRGNB). Methods: we collected all the CRGNB from September 2014 till January 2016, we took randomly 40/126 strains and performed a sensitivity test in addition to a real time multiplex PCR to detect the exact carbapenemase coding genes (bla SPC , bla IMP1, bla VIM , bla NDM , bla KPC , et bla OXA-48). The studied strains were: Escherichia coli (70%), Klebsiella pneumonia (20%), Enterobacter aerogenes (2,5%), Enterobacter cloacae (2.5%) et Klebsiella oxytoca (2.5%). Results: 100% of the studied strains were intermediate or resistant to ertapenem, 85% intermediate or resistant to imipenem and/or meropenem. 33 / 40 strains (82.5%) are bla OXA-48 positive et one strain (2.5%) is bla NDM positive. the OXA-48 were urinary strains of E coli. 6 / 40 strains (15%) did not express carbapenemase genes in molecular studies. Conclusion: we note a marked emergence of CPGNB especially bla OXA-48 with high resistance pattern leading to narrow therapeutic options. This requires a rapid detection of such strains of GNB so that to initiate quickly the right preventive and therapeutic measures to avoid hospital epidemics with disastrous consequences.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".