Non‐tuberculous mycobacteria in children with cystic fibrosis: Isolation, prevalence, and predictors
Bibliographic record
Abstract
BACKGROUND: Screening for non-tuberculous mycobacteria (NTM) is recommended for adults with cystic fibrosis (CF). The relevance of this organism in North American pediatric CF patients is unclear as there is limited NTM prevalence data for children. We aimed to determine the prevalence of NTM in children with CF from a single expectorated sputum and identify clinical predictors of NTM isolation. Additionally, we compared two different sputum decontamination methods before mycobacterial culture. METHODS: From March to November 2004, all sputum-producing patients aged 6-18 years attending the CF clinic at the Hospital for Sick Children in Toronto, Canada, were screened for NTM. Sputum samples were processed by both a single (N-acetyl-l-cysteine + NaOH) and double (N-acetyl-l-cysteine + NaOH + oxalic Acid) decontamination method. Using our CF clinic database and patient charts we analyzed differences in FEV(1), age, sex, pancreatic sufficiency, body mass index, bacterial colonization, and antibiotic use between NTM positive and negative patients. RESULTS: Of 98 study patients, 6 (6.1%) were positive for NTM, 2 with Mycobacterium abscessus, and 4 with Mycobacterium avium complex. One patient with M. abscessus had clinically significant lung disease requiring treatment. We found no predictors of NTM isolation. The double decontamination method allowed detection of only half (3/6) of the positive NTM cultures. CONCLUSIONS: As the NTM prevalence rate in children with CF is within the range previously reported in adults and there are no reliable clinical predictors for isolation, annual sputum screening is needed to identify NTM in children. Further research is needed to determine the best sputum decontamination method for NTM culture in pediatric patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".