Bibliographic record
Abstract
PURPOSE OF REVIEW: Obesity-associated difficult asthma continues to be a substantial problem and, despite a move to address treatable traits affecting asthma morbidity and mortality, it remains poorly understood with limited phenotype-specific treatments. The complex association between asthma, obesity, and inflammation is highlighted and recent advances in treatment options explored. RECENT FINDINGS: Obesity negatively impacts asthma outcomes and has a causal link in the pathogenesis of adult-onset asthma. Imbalance in the adipose organ found in obesity favours a pro-inflammatory state both systemically and in airways. Obesity may impact currently available asthma biomarkers, and obesity-associated asthma specific biomarkers are needed. Whilst surgical weight loss interventions are associated with improvements in asthma control and quality of life, evidence for pragmatic conservative options are sparse. Innovative approaches tackling obesity-mediated airway inflammation may provide novel therapies. The immunopathological mechanisms underlying obesity-associated asthma require further research that may lead to novel therapeutic options for this disease. However, weight loss appears to be effective in improving asthma in this cohort and focus is also needed on non-surgical treatments applicable in the real-world setting.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".