Influence of obesity on the prevalence and clinical features of asthma
Bibliographic record
Abstract
Obesity has been associated with an increased prevalence of asthma and poorer control of this disease. However, the mechanisms by which obesity can influence airway function and make asthma more difficult to control remain uncertain. The physiological changes associated with obesity can contribute to respiratory symptoms and these should be differentiated from those caused by asthma. Obesity can possibly influence the development of asthma through genetic, developmental, hormonal, neurogenic or mechanical influences. Breathing at low lung volumes and changes in the pattern of breathing in obese subjects may alter airway smooth muscle plasticity and airway function. The release by adipocytes of various cytokines and mediators such as Interleukin-6, TNF-alpha, eotaxin, and leptin, and the reduction of anti-inflammatory adipokines in obese subjects may possibly contribute to the development or increased clinical expression of asthma in promoting airway inflammation. Reduced asthma control and impaired response to asthma therapy have been reported in obese patients. Obesity-related co-morbidities such as Sleep Apnea and Gastro-esophageal reflux may also contribute to this poor control. Weight loss improves asthma control and reduces medication needs. Research is needed to better define the optimal management of obese asthmatic patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".