Laboratory detection of carbapenemases among Gram-negative organisms
Bibliographic record
Abstract
SUMMARY The carbapenems remain some of the most effective options available for treating patients with serious infections due to Gram-negative bacteria. Carbapenemases are enzymes that hydrolyze carbapenems and are the primary method driving carbapenem resistance globally. Detection of carbapenemases is required for patient management, the rapid implementation of infection prevention and control (IP&C) protocols, and for epidemiologic purposes. Therefore, clinical and public health microbiology laboratories must be able to detect and report carbapenemases among predominant Gram-negative organisms from both cultured isolates and direct from clinical specimens for treatment and surveillance purposes. There is not a “one size fits all” laboratory approach for the detection of bacteria with carbapenemases, and institutions need to determine what fits best with the goals of their antimicrobial stewardship and IP&C programs. Luckily, there are several options and approaches available for clinical laboratories to choose methods that best suits their individual needs. A laboratory approach to detect carbapenemases among bacterial isolates consists of two steps, namely a screening process ( e.g. , not susceptible to ertapenem, meropenem, and/or imipenem), followed by a confirmation test ( i.e. , phenotypic, genotypic or proteomic methods) for the presence of a carbapenemase. Direct from specimen testing for the most common carbapenemases generally involves detection via rapid, molecular approaches. The aim of this article is to provide brief overviews on Gram-negative bacteria carbapenem-resistant definitions, types of carbapenemases, global epidemiology, and then describe in detail the laboratory methods for the detection of carbapenemases among Gram-negative bacteria. We will specifically focus on the Enterobacterales, Pseudomonas aeruginosa , and Acinetobacter baumannii complex.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".