Impact of type 2 biomarkers on changes in subjective cough outcomes in chronic cough
Bibliographic record
Abstract
Background: Asthma and Non-Asthmatic Eosinophilic Bronchitis(NAEB) are commonly treated with inhaled corticosteroids(ICS) and bronchodilators. It is unclear if the change in cough patient reported outcomes(PRO) is associated with a corresponding reduction in type 2 biomarkers(T2B). Objective: Evaluate the relationship between improvements in cough PROs and improvements in T2B in CC patients treated with ICS/LABA. Methods: A prospective observational single center cohort study. Patients were treated with ICS/LABA based on results from methacholine challenge and T2B including blood eosinophils, Fractional Exhaled Nitric Oxide(FeNO) and % sputum eosinophils. Cough PROs included measuring Leicester Cough Questionnaire(LCQ), cough severity visual analogue scale(VAS). Results: 69 patients with CC (49females;mean age,55.7±14.5yrs) were recruited. 28 patients were treated with ICS/LABA. Of these, 9 patients were responders(32%) and reported LCQ and VAS improvements above the MID(VAS change -45.0mm and LCQ change 8.2)(Fig 1). No significant relationship was observed between changes in T2B and subjective cough outcomes. Reduction in cough in responders was associated with a numerical reduction of 18.55ppb in FeNO and sputum eosinophils by 1.6%. Conclusions: Treatment with ICS/LABA resulted in improvement in cough PROs only in 32% of patients with CC. Elevated T2B seem to poorly predict treatment responses in CC patients. erj;64/suppl_68/PA324/F1 F1 F1
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".