Food for thought: optimal diet in patients with asthma and chronic obstructive pulmonary disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Nutritional intake plays a major role in the management of lung health. This review provides the latest perspective on how dietary choices can modulate lung function in patients with chronic obstructive pulmonary disease (COPD) and asthma. RECENT FINDINGS: The pathophysiology of COPD and asthma is driven by oxidative stress and inflammation of the airways, which is exacerbated by modifiable risk factors such as cigarette smoking and diet. Various foods can influence patient symptoms; highly processed foods increase the production of reactive oxygen species that augment airway inflammation, whereas foods rich in antioxidants, fiber and protein combat oxidative stress and muscle wastage. Patients with COPD or asthma are at increased risk of developing metabolic comorbidities, including cachexia and obesity that complicate disease phenotypes, leading to greater symptom severity. While clinical findings suggest a role for antioxidant and macronutrient support of lung function, comprehensive translational and clinical studies are necessary to better understand the mechanisms underlying nutrient interaction and lung structure-function. SUMMARY: Understanding the nutritional requirements that protect lung health and support weight management in COPD and asthma is imperative to providing personalized dietary recommendations and reducing patient morbidity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".