The epithelial era of asthma research: knowledge gaps and future direction for patient care
Bibliographic record
Abstract
The Epithelial Science Expert Group convened on 18-19 October 2023, in Naples, Italy, to discuss the current understanding of the fundamental role of the airway epithelium in asthma and other respiratory diseases and to explore the future direction of patient care. This review summarises the key concepts and research questions that were raised. As an introduction to the epithelial era of research, the evolution of asthma management throughout the ages was discussed and the role of the epithelium as an immune-functioning organ was elucidated. The role of the bronchial epithelial cells in lower airway diseases beyond severe asthma was considered, as well as the role of the epithelium in upper airway diseases such as chronic rhinosinusitis. The biology and application of biomarkers in patient care was also discussed. The Epithelial Science Expert Group also explored future research needs by identifying the current knowledge and research gaps in asthma management and ranking them by priority. It was identified that there is a need to define and support early assessment of asthma to characterise patients at high risk of severe asthma. Furthermore, a better understanding of asthma progression is required. The development of new treatments and diagnostic tests as well as the identification of new biomarkers will also be required to address the current unmet needs. Finally, an increased understanding of epithelial dysfunction will determine if we can alter disease progression and achieve clinical remission.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".