Trends in oral corticosteroids use in severe asthma: a 14-year population-based study
Bibliographic record
Abstract
BACKGROUND: Oral corticosteroids are important components of pharmacotherapy in severe asthma. Our objective was to describe the extent, trends, and factors associated with exposure to oral corticosteroids (OCS) in a severe asthma cohort. METHODS: We used administrative health databases of British Columbia, Canada (2000-2014) and validated algorithms to retrospectively create a cohort of severe asthma patients. Exposure to OCS within each year of follow-up was measured in two ways: maintenance use as receiving on average ≥ 2.5 mg/day (prednisone-equivalent) OCS, and episodic use as the number of distinct episodes of OCS exposure for up to 14 days. Trends and factors associated with exposure on three time axes (calendar year, age, and time since diagnosis) were evaluated using Poisson regression. RESULTS: 21,144 patients (55.4% female; mean entry age 28.7) contributed 40,803 follow-up years, in 8.2% of which OCS was used as maintenance therapy. Maintenance OCS use declined by 3.8%/calendar year (p < 0.001). The average number of episodes of OCS use was 0.89/year, which increased by 1.1%/calendar year (p < 0.001). Trends remained significant for both exposure types in adjusted analyses. Both maintenance and episodic use increased by age and time since diagnosis. CONCLUSIONS: This population-based study documented a secular downward trend in maintenance OCS use in a period before widespread use of biologics. This might have been responsible for a higher rate of exacerbations that required episodic OCS therapy. Such trends in OCS use might be due to changes in the epidemiology of severe asthma, or changes in patient and provider preferences over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".