Risk Factors for Nontuberculous Mycobacterial Pulmonary Disease
Bibliographic record
Abstract
BACKGROUND: Nontuberculous mycobacterial pulmonary disease (NTM-PD) is widely underdiagnosed, and certain patient groups, such as those with underlying respiratory diseases, are at increased risk of developing the disease. Understanding patients at risk is essential to allow for prompt testing and diagnosis and appropriate management to prevent disease progression. RESEARCH QUESTION: What are the risk factors for NTM-PD that should prompt a physician to consider NTM testing and diagnosis? STUDY DESIGN AND METHODS: Electronic searches of PubMed and EMBASE were conducted in July 2021 for the period 2011-2021. Inclusion criteria were studies of patients with NTM-PD with associated risk factors. Data were extracted and assessed using the Newcastle-Ottawa Scale. Data analysis was conducted using the R-based "meta" package. Only studies that reported association outcomes for cases with NTM-PD compared with control participants (healthy populations or participants without NTM-PD) were considered for the meta-analysis. RESULTS: Of the 9,530 searched publications, 99 met the criteria for the study. Of these, 24 formally reported an association between possible risk factors and the presence of NTM-PD against a control population and were included in the meta-analysis. Comorbid respiratory disease was associated with a significant increase in the OR for NTM-PD (bronchiectasis [OR, 21.43; 95% CI, 5.90-77.82], history of TB [OR, 12.69; 95% CI, 2.39-67.26], interstitial lung disease [OR, 6.39; 95% CI, 2.65-15.37], COPD [OR, 6.63; 95% CI, 4.57-9.63], and asthma [OR, 4.15; 95% CI, 2.81-6.14]). Other factors noted to be associated with an increased risk of NTM-PD were the use of inhaled corticosteroids (OR 4.46; 95% CI, 2.13-9.35), solid tumors (OR, 4.66; 95% CI, 1.04-20.94) and the presence of pneumonia (OR, 5.54; 95% CI, 2.72-11.26). INTERPRETATION: The greatest risk for NTM-PD is conferred by comorbid respiratory diseases such as bronchiectasis. These findings could help with identification of patient populations at risk for NTM-PD to drive prompt testing and appropriate initiation of therapy.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".