Matrix-assisted laser desorption/ionization time-of-flight MS for the accurate identification of Burkholderia cepacia complex and Burkholderia gladioli in the clinical microbiology laboratory
Bibliographic record
Abstract
Introduction. Burkholderia cepacia complex (Bcc) bacteria, currently consisting of 23 closely related species, and Burkholderia gladioli , can cause serious and difficult-to-treat infections in people with cystic fibrosis. Identifying Burkholderia bacteria to the species level is considered important for understanding epidemiology and infection control, and predicting clinical outcomes. Matrix-assisted laser desorption/ionization time-of-flight MS (MALDI-TOF) is a rapid method recently introduced in clinical laboratories for bacterial species-level identification. However, reports on the ability of MALDI-TOF to accurately identify Bcc to the species level are mixed. Aim. The aim of this project was to evaluate the accuracy of MALDI-TOF using the Biotyper and VITEK MS systems in identifying isolates from 22 different Bcc species and B. gladioli compared to recA gene sequencing, which is considered the current gold standard for Bcc. Methodology. To capture maximum intra-species variation, phylogenetic trees were constructed from concatenated multi-locus sequence typing alleles and clustered with a novel k-medoids approach. One hundred isolates representing 22 Bcc species, plus B. gladioli , were assessed for bacterial identifications using the two MALDI-TOF systems. Results. At the genus level, 100 and 97.0 % of isolates were confidently identified as Burkholderia by the Biotyper and VITEK MS systems, respectively; moreover, 26.0 and 67.0 % of the isolates were correctly identified to the species level, respectively. In many, but not all, cases of species misidentification or failed identification, a representative library for that species was lacking. Conclusion. Currently available MALDI-TOF systems frequently do not accurately identify Bcc bacteria to the species level.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".