Targeted Treatment in Asthma—Opportunities and Challenges
Bibliographic record
Abstract
This review summarizes the key insights and future directions on targeted asthma treatment discussed during the 2025 Expert Meeting supported by the Central and Southern European Allergy and Asthma Alliance (CSEA3). Targeted treatment in asthma is becoming an attainable goal for selected patients but is not yet established as a standard asthma care pathway. The expert panel identified 4 key priorities to advance targeted asthma management: (1) Defining remission-a universally accepted, evidence-based definition is needed to guide clinical practice and guideline development. (2) The management of mild asthma guided by patient stratification according to asthma pathogenetic pathways (endotype) and risk profile may enable more tailored therapy and better outcomes. (3) Biomarker discovery and validation-research must prioritize predictive biomarkers that are easy to measure at the point of care, supported by innovative trials that combine precision immunology and machine learning. (4) Optimizing implementation and addressing the barriers to adopting stratified care, including limited resources and cost-effectiveness concerns, must be addressed. Digital tools offer promise but require further validation. Coordinated efforts are essential to translate advances in personalized asthma treatment into better outcomes and more sustainable care, particularly in resource-limited settings.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".