Evaluation of Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry and ClinPro Tools as a Rapid Tool for Typing Streptococcus pyogenes
Bibliographic record
Abstract
Abstract Background Timely strain typing of group A Streptococci (GAS) is necessary to guide outbreak recognition and investigation. We evaluated the use of (matrix-assisted laser desorption ionization time-of-flight mass spectrometry) combined with cluster analysis software to rapidly distinguish between related and unrelated GAS isolates in real-time. Methods We developed and validated a typing model using 177 GAS isolates with known emm types. The typing model was created using 43 isolates, which included 8 different emm types, and then validated using 134 GAS isolates of known emm types that were not included in model generation. Results Twelve spectra were generated from each isolate during validation. The overall accuracy of the model was 74% at a cutoff value of 80%. The model performed well with emm types 4, 59, and 74 but showed poor accuracy for emm types 1, 3, 12, 28, and 101. To evaluate the ability of this tool to perform typing in an outbreak situation, we evaluated a virtual outbreak model using a “virtual outbreak strain; emm74” compared with a non-outbreak group or an “outgroup “ of other emm types. External validation of this model showed an accuracy of 91.4%. Conclusions This approach has the potential to provide meaningful information that can be used in real time to identify and manage GAS outbreaks. Choosing isolates characterized by whole genome sequencing rather than emm typing for model generation should improve the accuracy of this approach in rapidly identifying related and unrelated GAS strains.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".