Bibliographic record
Abstract
PURPOSE OF REVIEW: Asthma is heterogeneous with different endotypes/phenotypes. Response to corticosteroids is variable and novel biological therapies are proving useful. Biomarkers allow individualization of treatment. This review provides an update on available data regarding asthma biomarkers with focus on their utility for prediction of response to steroidal and new biological therapies. RECENT FINDINGS: Blood eosinophils are a biomarker with acceptable accuracy as a surrogate for sputum eosinophilia, are associated with relevant outcomes, and are more readily measureable. New evidence supports fraction of exhaled nitric oxide (FENO)-based treatment algorithms for cost-effective maintenance of asthma control/quality of life. Serum and sputum-derived periostin are biomarkers of lung function decline and associated with eosinophilic airway inflammation. Transcriptomics show promise for endotyping; their role in management remains to be determined. Biomarker panels may improve predictive value as shown for the combination of FENO/urinary bromotyrosine in prediction of steroid responsiveness. Novel biological therapies are proving effective in biomarker-selected populations. SUMMARY: Biomarkers including blood eosinophils and FENO are proving to have utility for the effective administration of steroidal and novel biological therapies in asthma, allowing individualized treatment. Transcriptomics can discriminate subtypes of asthma and may have a role in delivery of individualized therapy.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".