Combination of modified carbapenem inactivation method (mCIM) and EDTA-CIM (eCIM) for phenotypic detection of carbapenemase-producing Enterobacteriaceae
Bibliographic record
Abstract
BACKGROUND: Carbapenemase-resistant Enterobacteriaceae (CRE) cause many serious infections resulting in increasing treatment cost, prolonged hospitalization, and mortality rate. Reduced expression and/or mutations of porins and the presence of carbapenemase promote Enterobacteriaceae survival under carbapenem treatments. Development of accurate methods for the detection of antimicrobial resistance is required not only for therapy but also to monitor the spread of resistant bacteria or resistance genes throughout the hospital and community. In this study, we aimed to evaluate the phenotypic methods, Modified Hodge test (MHT), modified carbapenem inactivation method (mCIM), and EDTA-CIM (eCIM) for the detection of carbapenemase-producing Enterobacteriaceae (CPE). RESULTS: The results showed that mCIM had a sensitivity of 100% and a specificity of 100%, whereas the MHT had a sensitivity of 84.8% and a specificity of 97.8% for the 195 CRE isolates tested (105 CPE and 90 non-CPE isolates). The sensitivity of the mCIM/eCIM to detect metallo-carbapenemases in this study was 89.3% and the specificity was 98.7% as compared to the genotypic PCR detection. CONCLUSIONS: These findings indicate that the mCIM combined with eCIM is useful for detecting and distinguishing different types of carbapenemase in Enterobacteriaceae.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".