Comparison of sequential multiplex PCR, sequetyping and whole genome sequencing for serotyping of Streptococcus pneumoniae
Bibliographic record
Abstract
Streptococcus pneumoniae is one of the major causes of pneumonia, meningitis and other pneumococcal infections in young children and elders. Determination of circulating S. pneumoniae serotypes is an essential service by public health laboratories for the monitoring of putative serotype replacement following the introduction of pneumococcal conjugate vaccines (PCVs) and of the efficacy of the immunization program. The Quellung method remains the gold standard for typing S. pneumoniae. Although this method is very effective, it is also costly, time consuming and not totally reliable due to its subjective nature. The objectives of this study were to test and evaluate the efficiency of 3 different molecular methods compared to the Quellung method. Sequential multiplex PCR, sequetyping and whole genome sequencing (WGS) were chosen and tested using a set of diverse S. pneumoniae. One-hundred and eighteen isolates covering 83 serotypes were subjected to multiplex PCR and sequetyping while 88 isolates covering 53 serotypes were subjected to WGS. Sequential multiplex PCR allowed the identification of a significant proportion (49%) of serotypes at the serogroup or subset level but only 27% were identified at the serotype level. Using WGS, 55% to 60% of isolates were identified at the serotype level depending on the analysis strategy used. Finally, sequetyping demonstrated the lowest performance, with 17% of misidentified serotypes. The use of Jin cpsB database instead of the GenBank database slightly improved results but did not significantly impact the efficiency of sequetyping. Although none of these molecular methods may currently replace the Quellung method, WGS remains the most promising molecular pneumococcal serotyping method.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".