Asthma healthcare burden with ICS/LABA or LAMA-containing therapies: Chinese retrospective cohort study
Bibliographic record
Abstract
Rationale: There is limited data available to quantify the burden of asthma in China associated with ICS/LABA or any LAMA-containing asthma therapies. We assessed the healthcare burden for Chinese patients with asthma. Methods: Retrospective cohort study in adult patients with asthma prescribed ICS/LABA or any LAMA-containing therapy between November 2019–February 2023 in the Tianjin electronic medical record database. Asthma healthcare resource utilisation and direct medical costs were assessed descriptively ≥12 months following first prescription. Results: In each cohort (ICS/LABA: n=42,802; LAMA: n=5423) over the observation period (median [interquartile range (IQR)] years ICS/LABA:2.80[1.99]; LAMA:2.22[1.55]) patients had median (IQR) annualised visits 0.90(1.42) in the ICS/LABA cohort and 0.83(1.26) in the LAMA cohort, most of which were outpatient visits (ICS/LABA:0.87[1.38]; LAMA:0.79[1.17]). The median (IQR) visit cost per patient per year was 421(796) CNY in the ICS/LABA cohort and 499(943) CNY in the LAMA cohort. Hospitalisations (median [IQR] annualised visit ICS/LABA:0.32[0.36]; LAMA:0.37[0.43]) were the most expensive visit type (ICS/LABA:3878[5562] CNY; LAMA:4789[7111] CNY) (Table). Conclusions: Burden for patients with asthma was mainly driven by outpatient visits. Economic burden on healthcare systems may be improved by reducing hospitalisations and disease burden. Funding: GSK(217511/217512) erj;66/suppl_69/PA4644/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) | 0.000 | 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 | 0.000 | 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".