Evaluation of digital dispense-assisted broth microdilution antimicrobial susceptibility testing for Pseudomonas aeruginosa isolates
Bibliographic record
Abstract
Abstract Antimicrobial susceptibility testing (AST) is essential for detecting resistance in Pseudomonas aeruginosa and other bacterial pathogens. Here we evaluated the performance of broth microdilution (BMD) panels created using a semi-automated liquid handler, the D300e Digital Dispenser (Tecan Group Ltd., CH) that relies on inkjet printing technology. Microtitre panels (96-well) containing nine twofold dilutions of 12 antimicrobials from five classes (β-lactams, β-lactam/β-lactamase inhibitors, aminoglycosides, fluoroquinolones, polymyxins) were prepared in parallel using the D300e Digital Dispenser and standard methods described by CLSI/ISO. To assess performance, panels were challenged with three well characterized quality control organisms and 100 clinical P. aeruginosa isolates. Traditional agreement and error measures were used for evaluation. Essential (EA) and categorical (CA) agreements were 92.7% and 98.0% respectively for P. aeruginosa isolates with evaluable on-scale results. The majority of minor errors that fell outside acceptable EA parameters (≥ ± 1 dilution, 1.9%) were seen with aztreonam (5%) and ceftazidime (4%), however all antimicrobials displayed acceptable performance in this situation. Differences in MIC were often log2 dilution lower for D300e dispensed panels. Major and very major errors were noted for aztreonam (2.6%) and cefepime (1.7%) respectively. The variable performance of D300e panels suggests that further testing is required to confirm their diagnostic utility 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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".