Evaluation of phenotypic and genotypic methods for the identification and characterisation of bacterial isolates recovered from catheter-associated urinary tract infections
Bibliographic record
Abstract
Abstract Purpose Urinary tract infections are the most common type of hospital-acquired infection, up to 80% of which are associated with catheterisation. The present study evaluates phenotypic and genomic characterisation of a panel of catheter associated urinary tract infection isolates from a UK hospital. Methods Strains were identified and characterised utilising a range of phenotypic and genomic techniques to understand where methodologies agree. The effect of medium composition on growth and biofilm formation phenotype was also determined to evidence the importance of assay design in characterisation of bacterial isolates. Results No consensus was observed for any of the CAUTI isolates across five identification methods, including biochemical testing, MALDI and sequencing technologies. Comparison of EUCAST antimicrobial susceptibility testing and genotypic data for antibiotic resistance showed high concordance where strains were phenotypically resistant to multiple antibiotic classes, however discordance increased for strains that were phenotypically sensitive to range of antibiotics. Phenotypic analysis of bacterial pathogens often relies on the use of rich laboratory media; however, we observed significant differences in growth rate and biofilm formation within a range of media, with a trend towards comparatively low planktonic growth and significant biofilm biomass formation in artificial urine. Conclusion This study emphasises potential pitfalls of relying on a single method of species identification, with only whole genome sequencing providing accurate identification of isolates to species level. Furthermore, it highlights the continuing importance of utilising phenotypic methods to understand antibiotic resistance within clinical settings and of utilising clinically relevant conditions for pathogen characterisation.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".