The geographic diversity of nontuberculous mycobacteria isolated from pulmonary samples: an NTM-NET collaborative study
Bibliographic record
Abstract
A significant knowledge gap exists concerning the geographical distribution of nontuberculous mycobacteria (NTM) isolation worldwide. To provide a snapshot of NTM species distribution, global partners in the NTM-Network European Trials Group (NET) framework (www.ntm-net.org), a branch of the Tuberculosis Network European Trials Group (TB-NET), provided identification results of the total number of patients in 2008 in whom NTM were isolated from pulmonary samples. From these data, we visualised the relative distribution of the different NTM found per continent and per country. We received species identification data for 20 182 patients, from 62 laboratories in 30 countries across six continents. 91 different NTM species were isolated. Mycobacterium avium complex (MAC) bacteria predominated in most countries, followed by M. gordonae and M. xenopi. Important differences in geographical distribution of MAC species as well as M. xenopi, M. kansasii and rapid-growing mycobacteria were observed. This snapshot demonstrates that the species distribution among NTM isolates from pulmonary specimens in the year 2008 differed by continent and differed by country within these continents. These differences in species distribution may partly determine the frequency and manifestations of pulmonary NTM disease in each geographical location.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".