Clinically relevant outcome measures for new therapies of asthma using pharmaceutical and biologic agents
Bibliographic record
Abstract
PURPOSE OF REVIEW: To determine the benefits of new asthma drugs or therapies, they should be assessed with regard to their effects on relevant clinical outcomes. RECENT FINDINGS: The most frequently used outcomes have been symptoms, rescue medication needs and pulmonary function tests, although others such as quality of life, exacerbations and impairment of activities have also been identified as important ones. Improvements in our understanding of basic mechanisms of asthma have led to the development of new sets of outcomes including inflammatory markers and a rapidly increasing number of biomarkers, which however require validation, and assessment of their clinical usefulness. Many studies have not only looked at induced sputum cell differentials or FENO to phenotype asthma but also as treatment efficacy markers. Periostin is considered a marker of TH2-induced airway inflammation and a predictor of response to drugs such as anti-IL13 and omalizumab, although at the individual level, such prediction remains imperfect. SUMMARY: There is a need to develop new markers of activity of the disease, with a prognostic value with regard to the benefits of new treatments.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".