Pediatric Non-Cystic Fibrosis Pulmonary Nontuberculous Mycobacterium Infections: A Global Population Based Study
Bibliographic record
Abstract
Marina Bahaa Monir Zakhary Gad El Sayed,1 Dennis Tai,2,* Lucy Yu,3,* Daniel Novak,1 Amrita Dosanjh4 1School of Medicine, University of California Riverside, Riverside, CA, USA; 2Department of Biology, Brown University, Providence, RI, USA; 3School of Public Health, Brown University, Providence, RI, USA; 4Pediatric Respiratory, Affiliated Rady Children’s Hospital, San Diego, CA, USA*These authors contributed equally to this workCorrespondence: Amrita Dosanjh, Pediatric Respiratory, Affiliated Rady Children’s Hospital San Diego, San Diego, CA, USA, Email pulmd1@gmail.comBackground: Nontuberculous mycobacteria (NTM) are Mycobacterial pathogens that cause pulmonary infections among children, particularly those with underlying lung conditions or immunosuppression. Clinical presentations include chronic cough, weight loss, and fatigue. Diagnosis involves clinical assessment, radiographic imaging, and microbiological confirmation, while treatment often requires prolonged, multidrug antibiotic regimens. This study aimed to analyze the epidemiology and clinical outcomes of pulmonary NTM infections in a non-cystic fibrosis pediatric population from four distinct age groups.Methods: A retrospective study as cross-sectional design for data collection from the TriNetX platform, a global electronic health record database. Inclusion criteria targeted pediatric patients aged 0– 18 years with pulmonary NTM, while exclusion criteria included cystic fibrosis, tuberculosis, smoking history, and cutaneous NTM infections. The cohort comprised 109 cases among 0– 2 years (mean age 2 years), 401 cases among 3– 5 years (mean age 4 years), 1,074 cases among 6– 12 years (mean age 9 years), and 760 cases among 13– 18 years (mean age 15 years). Demographics, comorbidities, and inflammatory markers were analyzed. Logistic and binomial regression models were used to evaluate associations between age group and five-year outcomes of pediatric pulmonary NTM, reporting odds ratios (OR), risk ratios (RR), 95% confidence intervals (CI), and p-values.Results: Of the total 2,344 records of pediatric patients examined, the most common comorbidities included malignancies (36%), acute pharyngitis (78%), asthma (46%), unspecified pneumonia (46%), and immunodeficiencies (22%). Female patients represented 53.31% of cases. Key inflammatory markers (eg C-reactive protein (CRP), mean white blood cell count, ferritin) were elevated among older age groups.Conclusion: This study highlights age-specific variations in risk factors, clinical outcomes, and inflammatory responses, offering potential insights for improved diagnosis and management of NTM in children. These results underscore the importance of further research in pediatric cohorts with NTM to better understand its role in pediatric pulmonary conditions and comorbidities.Keywords: non-tuberculous mycobacteria, non-cystic fibrosis, pulmonary, pediatric, NTM-PD
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".