Evaluation of the rapid ResaPolymyxin Acinetobacter/Pseudomonas NP test for rapid colistin resistance detection in lactose non-fermenting Gram-negative bacteria
Bibliographic record
Abstract
Introduction. Colistin is one of the last-resort antibiotics for treating multidrug-resistant (MDR) or extensively drug-resistant (XDR) lactose non-fermenting Gram-negative bacteria such as Pseudomonas aeruginosa and Acinetobacter baumannii . Gap Statement. As the rate of colistin resistance is steadily rising, there is a need for rapid and accurate antimicrobial susceptibility testing methods for colistin. The Rapid ResaPolymyxin Acinetobacter / Pseudomonas NP test has recently been developed for rapid detection of colistin resistance in P. aeruginosa and A. baumannii . Aim. The present study aimed to evaluate the performance of the Rapid ResaPolymyxin Acinetobacter / Pseudomonas NP test in comparison with the reference broth microdilution (BMD) method. Methodology. The Rapid ResaPolymyxin Acinetobacter / Pseudomonas NP test was performed using a total of 135 P . aeruginosa (17 colistin-resistant and 118 colistin-susceptible) and 66 A. baumannii isolates (32 colistin-resistant and 34 colistin-susceptible), in comparison with the reference BMD method. Results. The categorical agreement of the Rapid ResaPolymyxin Acinetobacter / Pseudomonas NP test with the reference BMD method was 97.5 % with a major error rate of 0 % (0/152) and a very major error (VME) rate of 10.2 %. The VME rate was higher (23.5 %) when calculated separately for P. aeruginosa isolates. The overall sensitivity and specificity were 89.8 and 100 %, respectively. Conclusion. The Rapid ResaPolymyxin Acinetobacter / Pseudomonas NP test performed better for A. baumannii than for P. aeruginosa .
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".