Cutaneous Nontuberculous Mycobacterial Infections in Alberta, Canada: An Epidemiologic Study and Review
Bibliographic record
Abstract
BACKGROUND: Cutaneous infections caused by nontuberculous mycobacteria (NTM) occur infrequently. Nonetheless, the incidence of NTM infections is reported to be increasing. In Canada, cutaneous NTM infections have not been well described. OBJECTIVES: A database review from 2006 to 2016 was done to assess species frequency, incidence, and trends of the most common cutaneous NTMs in the province of Alberta, Canada. We also reviewed major diagnostic and epidemiologic aspects of NTM cutaneous infections with a focus on Mycobacterium marinum. RESULTS: A database search identified 244 cases of NTM infections. Mycobacterium avium-intracellulare complex had the highest incidence, causing 64% of cases. Rapid growers ( Mycobacterium abscessus, Mycobacterium chelonae, Mycobacterium fortuitum) caused 23% and M marinum 13%. Information on infection site was available for 117 cases. There was no difference noted in sex distribution; however, differences in age groups between species were noted. CONCLUSIONS: The incidence of NTM cutaneous infections in Alberta, Canada, was reported for the first time and the incidence of M marinum was found to be similar to that reported in the worldwide literature. Patients' age groups were different between species. Knowledge of the unique microbiological features of NTMs and the role of the diagnostic laboratory are important.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".