Real-life asthma impairment: SABA use depends on smoking phenotype
Bibliographic record
Abstract
Background : Asthma impairment is defined by short-acting beta-agonist (SABA) use, symptoms, functional limitations, and pulmonary function. Objective measures of impairment rely on SABA records but the Respiratory Effectiveness Group believe SABA use may vary by management step and smoking status. Methods: Real-life study pooling records from the UK’s Optimum Patient Care & Clinical Practice Research Databases for asthma patients aged 16–70yrs. Average daily SABA use was evaluated over one year (total annual dose prescribed/365) and split by management step (inhaled corticosteroid [ICS] initiation or step up) and smoking status (current, non- and ex-smoker). Results: For both ICS initiation and step-up populations, the distribution of SABA dosage varied significantly by smoking status (p<0.001). More current smokers used ≥800µg, and fewer ≤200µg SABA daily than non- or ex-smokers. ICS Initiation* Smoking status SABA Daily Dosage (µg) Non smoker n(%) Ex-smoker n(%) Current smoker n(%) ≤200 20169 (52.4) 11886 (52.1) 10106 (39.5) 201-800 16288 (42.3) 9713 (42.6) 12643 (49.3) ≥800 2081 (5.4) 1213 (5.3) 2884 (11.3) *p<0.001 (SABA distribution by smoking status) ICS Step-up* Smoking Status SABA Daily Dosage (µg) Non smoker n(%) Ex-smoker n(%) Current smoker n(%) ≤200 7612 (35.6) 3460 (33.9) 2349 (21.9) 201-800 10836 (50.6) 5200 (51.0) 5529 (51.4) ≥800 2950 (13.8) 1544 (15.1) 2873 (26.7) *p<0.001 (SABA distribution by smoking status) Conclusions: Average SABA use is higher among ICS step-up compared with ICS initiation patients. Current smokers use more SABA than non- or ex-smokers, irrespective of management step. Further work is required to set meaningful SABA thresholds for objective measures of real-life asthma impairment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".