Chinese patients receiving ICS/LABA or LAMA-containing asthma therapy characterisation: Retrospective cohort study
Bibliographic record
Abstract
Rationale: Asthma is a heterogeneous disease with 2.2% prevalence in China. We detailed characteristics, clinical burden and treatment patterns of patients with asthma treated with ICS/LABA or LAMA-containing therapy. Methods: Retrospective cohort study of adults with asthma identified in the Tianjin electronic medical record database. Patients with ≥1 prescription for ICS/LABA or LAMA-containing therapy (index: first prescription) November 2019–February 2023 were included. Demographic/clinical characteristics at index, disease burden 1 year post index (study period) and treatment patterns 1 year pre- and post-index were assessed descriptively. Results: Half of patients were female (ICS/LABA:22,277/42,802; LAMA:2665/5423), most were non-smokers (ICS/LABA:83.1%; LAMA:77.2%). Post index, 6.3% (median[Q1–Q3] annual exacerbation rate[AER]:0.31[0.23–0.54]) of ICS/LABA and 8.5% (0.38[0.26–0.69]) of LAMA patients had severe exacerbations. Median AER was 1.62 vs 0.65 when switching from ICS/LABA to LAMA cohort. In the study period, the most common comorbidity in ICS/LABA patients was respiratory tract infection (28.1%); for LAMA, it was COPD (46.8%). ICS/LAMA/LABA was used by 8.1% of ICS/LABA patients and 65.2% for LAMA. Duration of use and adherence was generally low (PDC<30%) (Table). Conclusions: Optimised asthma treatment to improve exacerbations and adherence in China is needed. Funding GSK(217511;217512) erj;66/suppl_69/PA2459/F1 F1 F1
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".