Evaluation of phenotypic and genotypic methods for the identification and characterization of bacterial isolates recovered from catheter-associated urinary tract infections
Bibliographic record
Abstract
AIMS: Urinary tract infections are the most common hospital-acquired infection, 80% of which are associated with catheterization. Diagnostic methods may influence the reported identities of these pathogens, and phenotypic testing under laboratory conditions may not reflect infection phenotypes. This study aimed to evaluate the efficacy of diagnostic methods and whether medium composition alters phenotypes by characterizing catheter-associated urinary tract infection isolates from a UK hospital. METHODS AND RESULTS: We compared five bacterial identification methods, including biochemical testing, matrix-assisted laser desorption/ionization biotyping, and genome sequencing, finding differences in genus- or species-level identifications. Antibiotic susceptibility comparisons between phenotypic assays and genomic predictions showed high agreement only in multidrug-resistant strains. To determine whether growth rate and biofilm formation were affected by medium composition, strains were grown in both planktonic and biofilm states. Low planktonic growth and significant biofilm formation were observed in artificial urine compared to rich laboratory media, underscoring the importance of assay design. CONCLUSIONS: This study highlights the risks of relying on a single diagnostic method for species identification, advocating for whole-genome sequencing for accuracy. It emphasizes the continued importance of phenotypic methods in understanding antibiotic resistance in clinical settings and the need for characterization conditions that mirror those encountered by pathogens in the body.
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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 | 0.010 | 0.020 |
| 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 | 0.000 |
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".