Managing T2-High Inflammation in Severe Asthma - Are Biomarkers Better Than Clinician Judgement?
Bibliographic record
Abstract
Introduction: Biomarkers are used to select patients for therapy in severe asthma, but not to regularly adjust therapy, especially oral corticosteroids (OCS). Our goal was to test the efficacy of an algorithm to guide the titration of OCS in adults with severe asthma using blood eosinophil count and fraction of exhaled nitric oxide levels (FeNO). Methods: This proof-of-concept randomized controlled trial assigned severe asthma participants (n=32) to an inflammation arm; where OCS dose was adjusted based on blood eosinophil count and FeNO, or a best-practice clinical care arm. The primary outcome was number of severe exacerbations and time to first severe exacerbation assessed over 12 months. Results: The relative risk of a severe exacerbation in the inflammation versus control arms was 0.88 (Adj.; 95%CI:0.47, 1.62; p=0.675) with a mean exacerbation rate per year of 2.5 and 3.5, respectively. There was a trend toward a longer median time to first severe exacerbation in the inflammation arm (73 vs. 32 days, Adj. HR:0.714; 95%CI:0.25, 2.06; p=0.533). The odds ratio of an emergency department (ED) or hospital admission in the inflammation management arm was 0.177 (Adj; 95%CI: 0.023, 1.361; p=0.0961). There was no significant difference in mean OCS dose used over the course of the study between the two groups, with both groups decreasing their doses by 0.1mg/day/visit. Conclusion: A treatment algorithm to adjust oral corticosteroids using blood eosinophil count and FeNO is feasible in a clinical setting and resulted in a longer time to exacerbation and reduced odds of a hospital admission or ED visit. This warrants further study to optimize the use of oral corticosteroids in the future.
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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 | 0.059 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".