Exploring the Long-Term Utility of Remotely Monitored FeNO Suppression Testing in Severe Asthma
Bibliographic record
Abstract
BACKGROUND: Confirmation of optimal inhaled corticosteroid use is essential before initiating biologic therapy. Fractional exhaled nitric oxide (FeNO) suppression testing (FeNOSuppT) is a proven phenotyping technique; however, its long-term effect on clinical outcomes remains unclear. OBJECTIVES: To assess the real-world feasibility of delivering FeNOSuppT alongside digital inhaler monitoring and to examine its effect on biologic initiation and clinical outcomes. METHODS: Prospective cohort study within 7 U.K. severe asthma centers. Patients received a sensor-enabled inhaled corticosteroid/long-acting β-agonist (ICS/LABA) inhaler during an initial appointment between July 2020 and June 2022. A positive FeNOSuppT was defined as greater than 42% FeNO reduction at short-term follow-up (typically 1-3 mo postbaseline). Biologic initiation and clinical outcomes were compared at short-term and long-term (typically 12 mo postbaseline) follow-up. RESULTS: (11.0% vs 2.3%; P = .016), and a similar reduction in both asthma symptoms (ACQ6 0.7 vs 0.8; P = .623) and exacerbations (66.7% vs 66.7%; P = .349) at long-term follow-up when compared with those with a negative FeNOSuppT. CONCLUSIONS: Delivering FeNOSuppT aligned with digital monitoring is feasible within routine care. A positive FeNOSuppT was associated with lower rates of biologic initiation, with similar clinical outcomes.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 0.000 |
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".