Evaluating the timing of triple therapy initiation for the treatment of asthma in Japan: prompt versus delayed
Bibliographic record
Abstract
Objective In Japan, the optimal initiation timing and efficacy of single-inhaler triple therapy (SITT) in asthma management remain unexplored. This study investigated SITT initiation timing following an asthma exacerbation, and examined patient demographics and clinical characteristics.Methods Observational, retrospective cohort study in patients with asthma aged ≥15 years who initiated SITT following their earliest observed asthma exacerbation (February–November 2021), using data from Japanese health insurance claims databases (JMDC and Medical Data Vision [MDV]). The study period ended May 2022 for JMDC and September 2022 for MDV. Descriptive analyses were performed independently by database. Variables evaluated included timing of SITT initiation post exacerbation (prompt, delayed and late, ≤30, 31–180 and >180 days post index, respectively), patient demographics, clinical characteristics, and pre-index treatment.Results Of patients in the JMDC and MDV databases, most initiated SITT promptly after an asthma exacerbation, 60.8% (n = 951/1565) and 44.4% (n = 241/543), respectively. Delayed initiation occurred in 22.6% (n = 354/1565) and 26.3% (n = 143/543) of patients, and late initiation occurred in 16.6% (n = 260/1565) and 29.3% (n = 159/543), respectively. Most patients were indexed on a moderate asthma-related exacerbation, 97.1% (n = 1519/1565) and 68.7% (n = 373/543), respectively.Conclusion Most patients with asthma initiated SITT promptly following a moderate exacerbation, with delayed and late initiation more common among patients with complex clinical profiles. The findings underscore the necessity for future research to examine the interaction between patient characteristics, clinical outcomes, and the timing of SITT initiation to optimize treatment strategies, as clinical practice may vary by exacerbation severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".