Bibliographic record
Abstract
The prevalence and incidence of asthma have increased among obese children and adults, particularly among women. Obesity seems to be a predisposing factor for the development of asthma, but the underlying mechanisms of its influence are still uncertain. Various hypotheses have been proposed to explain the link between obesity and asthma such as a common genetic predisposition, developmental changes, altered lung mechanics, the presence of a systemic inflammatory process, and an increased prevalence of associated comorbid conditions. Over-diagnosis of asthma does not seem to be more frequent in obese compared to non-obese subjects, but the added effects of obesity on respiratory symptoms can affect asthma control assessment. Obesity can make asthma more difficult to control and is associated with a reduced beneficial effect of asthma medications. This could be due to a change in asthma phenotype, particularly evidenced as a less eosinophilic type of airway inflammation combined to the added effects of changes in lung mechanics. Weight loss is associated with a universal improvement of asthma and should be part of asthma management in the obese patient. Additional research should be conducted to better determine how obesity influences the development and clinical expression of asthma, establish the optimal management of asthma in this population and determine how obesity affects long-term asthma outcomes in these 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".