A multi-site, international laboratory study to assess the performance of penicillin susceptibility testing of Staphylococcus aureus
Bibliographic record
Abstract
Abstract\n \n Objectives\n There is clinical uncertainty over the optimal treatment for penicillin-susceptible Staphylococcus aureus (PSSA) infections. Furthermore, there is concern that phenotypic penicillin susceptibility testing methods are not reliably able to detect some blaZ-positive S. aureus.\n \n \n Methods\n Nine S. aureus isolates, including six genetically diverse strains harbouring blaZ, were sent in triplicate to 34 participating laboratories from Australia (n = 14), New Zealand (n = 6), Canada (n = 12), Singapore (n = 1) and Israel (n = 1). We used blaZ PCR as the gold standard to assess susceptibility testing performance of CLSI (P10 disc) and EUCAST (P1 disc) methods. Very major errors (VMEs), major error (MEs) and categorical agreement were calculated.\n \n \n Results\n Twenty-two laboratories reported 593 results according to CLSI methodology (P10 disc). Nineteen laboratories reported 513 results according to the EUCAST (P1 disc) method. For CLSI laboratories, the categorical agreement and calculated VME and ME rates were 85% (508/593), 21% (84/396) and 1.5% (3/198), respectively. For EUCAST laboratories, the categorical agreement and calculated VME and ME rates were 93% (475/513), 11% (84/396) and 1% (3/198), respectively. Seven laboratories reported results for both methods, with VME rates of 24% for CLSI and 12% for EUCAST.\n \n \n Conclusions\n The EUCAST method with a P1 disc resulted in a lower VME rate compared with the CLSI methods with a P10 disc. These results should be considered in the context that among collections of PSSA isolates, as determined by automated MIC testing, less than 10% harbour blaZ. Furthermore, the clinical relevance of phenotypically susceptible, but blaZ-positive S. aureus, remains unclear.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".