Could the DiversiLab® semi-automated repetitive-sequence-based PCR be an acceptable technique for typing isolates of <i>Pseudomonas aeruginosa</i>? An answer from our experience and a review of the literature
Bibliographic record
Abstract
Recently the DiversiLab® (DL) system (bioMérieux) was developed as an automated platform that uses repetitive element polymerase chain reaction (rep-PCR) technology for standardized, reproducible DNA fingerprinting of bacteria. The purpose of this study was to evaluate the usefulness of DL rep-PCR for typing Pseudomonas aeruginosa isolates. The performance of DL rep-PCR was compared with that of pulsed-field gel electrophoresis (PFGE) in a prospective multicenter study of patients with ventilator-associated pneumonia due to P. aeruginosa, conducted in 3 intensive care units over a 31-month period. In total, 203 P. aeruginosa isolates from 66 patients, from whom at least 2 consecutive respiratory samples each were collected more than 48 h apart, were typed using DL rep-PCR. Forty isolates (corresponding to 20 patients) were also typed using PFGE of SpeI-digested DNA. The typeability was 100% with DL rep-PCR and 95% with PFGE. The discriminatory power was close for DL rep-PCR and for PFGE (Simpson's diversity indices of 0.901 and 0.947, respectively). Insufficient agreement between DL rep-PCR and PFGE typing results was observed for the 40 selected isolates (adjusted Rand coefficient of 0.419), mostly due to isolates of the same DL rep-PCR type but of different PFGE types (adjusted Wallace coefficients of 0.306 for DL rep-PCR with PFGE, and of 0.667 for PFGE with DL rep-PCR). Considered together with published data, DL rep-PCR results should be interpreted with caution for the investigation of outbreaks caused by P. aeruginosa and evaluated in conjunction with epidemiological data.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".