Impact of pulmonary nontuberculous mycobacterial treatment on pulmonary function tests in patients with and without established obstructive lung disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: There is relatively little data regarding pulmonary function test (PFT) findings and impact of treatment on PFT in pulmonary nontuberculous mycobacterial (pNTM) disease. METHODS: We performed a retrospective study on pNTM patients. Clinical, radiographical, microbiological and PFT data were reviewed. Patients were divided into three groups based on pre-existing obstructive lung disease: (i) normal (no chronic obstructive pulmonary disease (COPD) or asthma); (ii) asthma; and (iii) COPD. We studied pre-treatment PFT and assessed for PFT changes after anti-mycobacterial therapy. RESULTS: A total of 96 patients fulfilled ATS disease criteria and had pre-treatment PFT (54 'normal', 18 asthma, 24 COPD). Most common causative NTM was Mycobacterium avium complex (76%), and radiographical disease type was nodular bronchiectasis (71%). Before therapy, all groups had PFT abnormalities, including obstruction, gas trapping and at least mildly low diffusion capacity of carbon monoxide (DLCO). Pre-treatment PFT abnormalities were more pronounced among patients with asthma and COPD. A total of 44 patients had >12 months anti-mycobacterial therapy and post-treatment PFT. There tended to be small and generally not statistically significant reductions in spirometry and DLCO in most groups. Among the nine asthmatic patients, there was a small reduction in residual volume (RV) (1.5% predicted, P = 0.01) and RV/total lung capacity (by 7% predicted, P = 0.06). CONCLUSIONS: Patients with pNTM have abnormal PFT, and treatment was not associated with substantial changes therein. Asthmatics may experience some improvements in gas trapping after NTM therapy, but because the sample size and the observed change were both small, this requires further investigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".