Evaluating the accuracy of the MBT Lipid Xtract Kit for assessing colistin resistance in comparison to broth microdilution
Bibliographic record
Abstract
Colistin resistance testing methods such as broth microdilution (BMD) are time-consuming and labour intensive for clinical laboratories. MBT Lipid Xtract Kit on MALDI Biotyper Sirius System (Bruker, Billerica, MA, USA) utilizes lipidomic analysis to identify specific cell wall modifications associated with colistin resistance. We compared MBT to BMD (ComASP Colistin, Liofilchem) across 36 Gram-negative isolates (non-resistant MIC ≤2 µg ml−1, resistant MIC ≥4 µg ml−1). All samples were tested twice on MBT with discrepant results repeated before assessing categorical agreement between MBT and BMD. 44.4% (16/36) of isolates were colistin resistant via BMD. MBT Lipid Xtract had 80.6% agreement (29/36) with BMD, with 5/7 discrepancies corrected to match upon repeat testing. There was 100% agreement for Escherichia coli isolates (n=16). The whole-genome sequencing was completed on the two discrepant Klebsiella pneumoniae isolates, with variants within colistin resistance-associated loci identified (MIC 0.5 µg ml−1: arnC S30T, pmrB T246A, lapB N212T, lpxM S253G, crrB Q287K and MIC >16 µg ml−1: arnC S30T, pmrB R90insRN, pmrB T246A, pmrA E57G, lpxM S253G). Further evaluation, particularly for non-E. coli, of MBT is required prior to implementation in clinical laboratories.
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".