Assessing biomarkers in a real-world severe asthma study (ARIETTA)
Bibliographic record
Abstract
The prognostic value of asthma biomarkers in routine clinical practice is not fully understood. ARIETTA (NCT02537691) is an ongoing, prospective, longitudinal, international, multicentre real-world study designed to assess the relationship between asthma biomarkers and disease-related health outcomes. The trial aims to enrol and follow for 52 weeks approximately 1200 severe asthma patients from approximately 160 sites in more than 20 countries. Severe asthmatics, treated with daily inhaled corticosteroid (≥500 μg of fluticasone propionate or equivalent) and at least 1 second controller medication are to be included. In this real-world study, patients will be treated according to the investigator's routine clinical practices and no treatment regimen will be implemented as part of the trial. At baseline and again at 26 and 52 weeks, FEV1, FeNO, serum periostin, blood eosinophil count and serum IgE will be measured. Asthma-related symptom and quality of life questionnaires will be administered at the visits and during telephone interviews at Weeks 13 and 39. Data about medication use, asthma exacerbation data, asthma-related healthcare utilization and events raising safety concerns will also be collected. This study design, unique in both its scope and scale, will address fundamental unanswered questions regarding asthma biomarkers and their interrelationship, as well as predict deviations in the course of asthma in a real-world setting.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".