Differences between early-onset persistent and late-onset asthma: a real life study
Bibliographic record
Abstract
Background: Asthma can present in early age and progress through adulthood or de novo in adulthood. Aim: to investigate possible differences in pathogenetic and clinical features of early-onset asthma persisting into adulthood and late-onset asthma. Method: We conducted a cross-sectional study of 250 adult patients (54±16 years-old) recruited at our asthma clinic. We defined early-onset persistent asthma (EOA) as onset <12 years and late-onset asthma (LOA) as >40 years. Severity was graded by GINA steps (STEP1, 2, 3 controlled; STEP3 uncontrolled, 4 and 5). Results: 76/250 (30%) subjects had EOA and 98/250 (39%) LOA. Rhinitis was more frequent in EOA (76 vs 53%; p=0.02) and was associated with increased severity (p=0.01), reduced FEV1/FVC (74±10 vs 83±7%; p=0.01), increased eosinophils (0.36±0.3 vs 0.14±0.1 x109/L; p<0.01) and IgE (441±635 vs 71±76KU/L; p<0.01). IgE were directly related to blood eosinophils (r=0.42; p=0.005) and inversely to FEV1/FVC (r=-0.34;p=0.02) in EOA, but not in LOA. Conversely, LOA patients with rhinitis were less severe and had higher FEV1/FVC (82±9vs74±9%;p<0.01). Obesity was present in 20% of the 250 patients regardless onset time, but obesity in LOA was associated to a more severe disease (p=0.009), reduced FEV1/FVC (73±9 vs 80±10;p=0.009) and increased blood neutrophils (4.2±1.1 vs 3.7±1.6x109/L;p=0.03) when compared to non-obese LOA. GERD was highly prevalent (60%) but had no impact on disease severity or lung function. Bronchiectasis were rare and more predominant in LOA (9vs3%). Conclusions: Early-onset persistent and late-onset asthma are distinct phenotypes, with different underlying inflammatory patterns and different comorbidities which impact on the outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".