Nutrition and Markers of Disease Severity in Patients With Bronchiectasis
Bibliographic record
Abstract
BACKGROUND: Increasing numbers of patients are being diagnosed with bronchiectasis, yet much remains to be elucidated about this heterogeneous patient population. We sought to determine the relationship between nutrition and health outcomes in non-cystic fibrosis (non-CF) bronchiectasis, using data from the U.S. Bronchiectasis Nontuberculous Mycobacterial Research Registry (U.S. BRR). METHODS: This was a retrospective, observational, longitudinal study using 5-year follow-up data from the BRR. Bronchiectasis was confirmed on computed tomography (CT). We stratified patients into nutrition categories using body mass index (BMI), and correlated BMI to markers of disease severity. RESULTS: , non-tuberculous mycobacteria, or by cause of bronchiectasis. The majority of patients demonstrated stable BMI over 5 years. CONCLUSIONS: Although underweight patients with bronchiectasis have lower lung function, lower BMI does not appear to relate to other markers of disease severity in this patient population.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".