Increase in detection of Corynebacterium diphtheriae in Canada: 2006–2019
Bibliographic record
Abstract
Background: Increasingly, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) has been used to provide rapid, inexpensive and precise identification of bacteria, including Corynebacterium species.Only three Corynebacterium species are able to produce diphtheria toxin (DT), and strains recovered may be either toxin-producing or non-toxin-producing.It appears the more precise bacterial identification provided by MALDI-TOF systems has led to an increase in requests submitted to the National Microbiology Laboratory (NML) for toxin testing.Objective: To describe the number of isolates identified as C. diphtheriae, C. ulcerans and C. pseudotuberculosis, submitted to the NML between January 2006 and July 30, 2019, including their geographic area, source, and whether they produce DT.Methods: Referrals to the NML of human or animal isolates that were identified as any of those three Corynebacterium species were studied with respect to province, source and toxigenicity.Species identification was confirmed and then specimens were tested by polymerase chain reaction for the presence of tox genes and, if positive, for expression of DT by the modified Elek method.Analysis was descriptive.Results: Over the study period, 639 isolates were identified as C. diphtheriae, 22 isolates as C. ulcerans; no isolates were identified as C. pseudotuberculosis.There was an increase in C. diphtheriae referrals for DT testing: from eight per year in 2006 to an average of 15 per month in 2019, or a 1,200% increase over the 13.6-year period.The referrals were primarily from western Canada (n=609/639; 95%).Most (638/639, 99%) were human isolates and most were obtained from cutaneous sites.Of those isolates, 87/639 (13.6%) were found to be toxigenic and 552/639 (86.4%) non-toxigenic.Among C. ulcerans referrals, 17/22 (77%) were from humans and five (23%) were from animals, with 10/22 (45%) being toxigenic.Conclusion: There has been a marked increase in referrals to the NML for DT testing of Corynebacterium species.This could be due to the enhanced ability to identify these bacteria using MALDI-TOF systems.Ongoing monitoring will help to assess whether the increase is due solely to increased precision of diagnosis or whether these are emerging cutaneous pathogens.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".