Update on Clinical Inflammometry for the Management of Airway Diseases
Bibliographic record
Abstract
Airway inflammation is a central feature of many airway diseases such as asthma, chronic bronchitis, bronchiectasis and chronic cough; therefore, it is only logical that it is measured to optimize its treatment. However, most treatment recommendations, including the use of anti-inflammatory therapies such as corticosteroids, are based on assessments of only airflow and symptoms. Over the past 10 years, methods have been developed to assess airway inflammation relatively noninvasively. Quantitative cell counts in sputum and the fraction of exhaled nitric oxide are the most validated tests. Judicious use of currently available drugs, such as corticosteroids, bronchodilators and antibiotics, and other anti-inflammatory therapies guided by sputum eosinophil and neutrophil counts, have been demonstrated to decrease exacerbations of asthma and chronic obstructive pulmonary disease, ameliorate cough, improve quality of life in patients with these diseases and is cost effective compared with treatment strategies based on guidelines that do not incorporate these measurements. Thus, it is unfortunate that this is not used more widely in the management of airway diseases, particularly in patients with severe asthma and chronic obstructive pulmonary disease who experience frequent exacerbations.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".