Mycoplasma genitalium Infections and Associated Antimicrobial Resistance in Canada, 1980–2023
Bibliographic record
Abstract
Background: We aimed to describe trends in M. genitalium prevalence and associated resistance in Canada between 1980 and 2022. Methods: Ecological study and a scoping review. We collected publicly available data published by the governments of all Canadian provinces and territories. We also systematically searched PubMed, Medline, Embase, and grey literature using the keywords ‘M. genitalium’, ‘Canada’, and all provinces and territories. We reported M. genitalium prevalence, age, sex, gender, symptoms, coinfections, sample types used for diagnosis, and macrolide and fluoroquinolone resistance rates. Results: National or provincial surveillance systems for M. genitalium are absent. Eight studies reported the epidemiology of M. genitalium. The prevalence ranged between 3% in Quebec and 30.3% in Ontario. Half of the patients reported symptoms. The most collected sample for M. genitalium diagnosis was urine, followed by cervical and urethral swabs. Co-infection with Chlamydia trachomatis was reported in 3.3% to 16.4% of cases and with Neisseria gonorrhoeae in 0.0% to 24.0%. Macrolide resistance ranged between 25% and 82.1%, and fluoroquinolone resistance between 0.0% and 29.1%. Conclusions: M. genitalium prevalence and resistance rates varied by sex, gender, province, and specimen type. In the absence of routine surveillance, incomplete data hinders understanding the bacterium’s natural history, its impact on some key groups, and the tracking of antibiotic resistance.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".