Characterization of <i>Mycobacterium orygis</i>, <i>Mycobacterium bovis</i>, and <i>Mycobacterium caprae</i> Infections in Humans in Western Canada
Bibliographic record
Abstract
Epidemiologic research on zoonotic tuberculosis historically used Mycobacterium bovis as a surrogate measure; however, increased reports of human tuberculosis caused by other animal-associated Mycobacterium tuberculosis complex members like Mycobacterium orygis necessitates their inclusion. We performed a retrospective cohort study including persons infected with any animal-lineage M tuberculosis complex species in Alberta, Canada, from January 1995 to July 2021, identifying 42 patients (20 M bovis, 21 M orygis, 1 M caprae). Demographic, epidemiologic, and clinical characteristics were compared against persons with culture-confirmed M tuberculosis infection. The proportion of culture-positive infections caused by M orygis increased continuously from 2016 to 2020. Significantly more females at a higher median age were impacted by M orygis, with all patients originating from South Asia. Mycobacterium bovis caused significantly more extrapulmonary disease and disproportionately impacted young females, particularly those pregnant or postpartum. All infections were acquired abroad. These findings can aid in developing targeted public health interventions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".