Epidemiological situation of mycobacterioses in Ukraine and the worldwide at the beginning of the 21st century: A literature review
Bibliographic record
Abstract
The lack of a unified reporting system for clinical outbreaks of mycobacterioses makes it difficult to objectively assess the epidemiological situation and identify patterns in the epidemic process, despite the growing relevance of this issue in human and veterinary medicine. The aim of this review was to study the epidemiological and aetiopathogenetic aspects of mycobacterioses in Ukraine and other countries on different continents. A comparative-geographic method and epidemiological analysis method were used in the study. As a result, it was found that in Ukraine, mycobacterioses in humans are widespread, with 94% of cases manifesting as pulmonary forms, often forming mixed infections with tuberculosis, making them difficult to diagnose. The most common aetiological factor is M. avium complex and disseminated mycobacteriosis usually develops in HIV-infected patients. In most of the analysed countries (Japan, South Korea, Iran, Turkey, Pakistan, Saudi Arabia, Egypt, Oman, Kuwait, China, France, Great Britain, Italy, Greece, Czech Republic, Poland, USA, Canada, Brazil, Australia and several African countries) during the period 2000-2023, there was an observed increase in the incidence of lung diseases caused by non-tuberculous mycobacteria, including an 8-fold rise in South Korea; an annual growth of 8% in the USA; and a 2.3-fold increase in Queensland (Australia) from 11.1 pcm in 2001 to 25.88 pcm in 2016. It was established that the epidemiological features of mycobacterioses are the predominant infection of patients with rapidly growing mycobacteria; an increased risk of mycobacterial infection with increasing age; detection of M. avium complex, M. abscessus complex, M. kansasii and M. fortuitum as the most common cause of mycobacterioses
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".