Increase in detection of Corynebacterium diphtheriae in Canada: 2006–2019
Bibliographic record
Abstract
BACKGROUND: 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: , submitted to the NML between January 2006 and July 30, 2019, including their geographic area, source, and whether they produce DT. METHODS: genes and, if positive, for expression of DT by the modified Elek method. Analysis was descriptive. RESULTS: referrals, 17/22 (77%) were from humans and five (23%) were from animals, with 10/22 (45%) being toxigenic. CONCLUSION: 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".