Global burden of non-tuberculous mycobacteria in the cystic fibrosis population: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: People living with cystic fibrosis have an increased risk of lung infection with non-tuberculous mycobacteria (NTM), which is reportedly increasing. We conducted a systematic review of the literature to estimate the burden (prevalence and incidence) of non-tuberculous mycobacteria in the cystic fibrosis population. Methods: Electronic databases, registries, and grey literature sources were searched for cohort and cross-sectional studies reporting epidemiological measures (incidence and prevalence) of NTM infection or NTM pulmonary disease (NTM-PD) in cystic fibrosis. The last search was conducted in September 2021; we included reports since database creation and registry reports published since 2010. The methodological quality of studies was appraised with the Joanna Briggs Institute tool. A random-effects meta-analysis was conducted to summarize the prevalence of NTM infection, and the remaining results are presented in a narrative synthesis. Results: Ninety-five studies were included in this review. All 95 studies reported on NTM infection, and 14 of these also reported on NTM-PD. The pooled estimate for the point prevalence of NTM infection was 7.9% (CI 95%, 5.1–12.0%). In meta-regression, sample size and geographical location of the study modified the estimate. Longitudinal analysis of registry reports showed an increasing trend in NTM infection prevalence between 2010 and 2019. Conclusions: The overall prevalence of NTM infection in CF is 7.9% and is increasing over time based on international registry reports. Future studies should report screening frequency, microbial identification methods, and incidence rates of progression from NTM infection to pulmonary disease.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".