Combined physiological and inflammatory biomarker assessment for risk stratification in moderate-to-severe asthma
Bibliographic record
Abstract
Introduction: Oscillometry identifies small airways dysfunction as resistance between 5 and 20Hz (Rrs5-20) and area under reactance curve (AX). We hypothesised that integrating physiological indices such as Rrs5-20, AX and FEV1 with inflammatory biomarkers including FeNO and blood eosinophil count (BEC) might better stratify exacerbation risk in persistent asthma. Methods: 1012 moderate-to-severe asthma patients from the Oscillometry Asthma Registry (discovery cohort, n=617) and the ATLANTIS study ( NCT02123667 , validation cohort, n=395) were included. Results: In the discovery analyses, the adjusted odds ratio (aOR) [95%CI] for ≥1 exacerbation in the previous 12 months was 7.36 (3.63,14.93) p<0.001 for the triple FeNO≥25ppb, BEC≥300cells/µL and Rrs5-20≥0.10kPa/L/s phenotype, compared to 2.03 (1.26, 3.26) p<0.01 for the dual FeNO and BEC phenotype, with non-overlapping 95%CIs suggesting statistical significance (Figure). This was externally validated for exacerbations in the following 12 months in the prospective validation analyses (Figure). Analyses with AX (not shown) demonstrated similar results. Conclusion: The present results highlight the added value of fully characterizing asthma phenotypes using a combination of small airway physiology parameters and type 2 biomarkers. If further validated, this immune-physiological composite score could be used as a clinical risk-stratification tool. erj;66/suppl_69/PA3636/F1 F1 F1
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".