Discordance between asthma control parameters in patients with frequent vs infrequent asthma exacerbations
Bibliographic record
Abstract
Background: Some asthmatics seem predisposed to frequent exacerbations. One reason of such frequent exacerbations may be more discrepancy between respiratory symptoms, expiratory flows and airway inflammation, making these patients more at risk of exacerbations, as their inflammatory and physiological changes do not translate well into warning symptoms. Aim: Assess the prevalence of discrepancies between asthma control parameters in patients with frequent asthma exacerbations (FAE) compared to patients with infrequent asthma exacerbations (IAE). Methods: FAE (≥2 exacerbations in the past year) and IAE (<2 exacerbations in the past year) were recruited. They completed the Asthma Control Scoring System (ACSS). The % score obtained for the clinical (C; symptoms), physiological (P; FEV 1 ), and inflammatory (I; sputum eosinophil) criteria were compared. Discrepancy was defined as a >20% difference between any 2 scores. Results: Forty-six FAE (31F/15M, aged (mean±SD) 44±13y) and 54 IAE (31F/23M, aged 44±12y) were recruited. In the year preceding the study, 175 exacerbations (49 severe) were recorded in FAE compared to 32 (6 severe) in IAE. ACSS global score showed similar asthma control between FAE (77±14%) and IAE (81±12%). The prevalence of discrepancies was similar between FAE and IAE (C vs P: 39% vs 41%, respectively, C vs I: 30% vs 35%, respectively, P vs I: 44% vs 35%, respectively). Conclusions: Frequent exacerbations do not seem to be characterized by more prevalent discrepancies between asthma control parameters.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".