OCS use in uncontrolled severe asthma in Canada
Bibliographic record
Abstract
Introduction: In Canada, among the estimated 230,000–465,000 patients with severe asthma (SA), a subset face uncontrolled disease. Despite increased availability of biologic treatments (bx) for such patients, a proportion of patients still rely on oral corticosteroids (OCS). OCS use results in significant short- and long-term consequences emphasizing the need to improve understanding of the current treatment patterns for patients with SA in Canada. Aims: To characterise uncontrolled SA by examining OCS and bx claims patterns. Methods: This is part of ALERT, a retrospective descriptive study using longitudinal claims data from IQVIA’s Private Drug Plan database (2020–23). Adult patients with inferred asthma diagnoses were selected. Patients were further classified into those with SA and uncontrolled SA based on prescriptions for inhaler therapy or OCS, respectively. Regional variation was also assessed. Results: Of 88,377 patients with SA, 11,208 (12.7%) were deemed uncontrolled (60.9% female; mean age 56 years). Patients with uncontrolled SA had a mean of 4.4 OCS claims per patient, with 8% having ≥10 OCS claims over one year; furthermore, 72% had no bx claims. A heatmap indicates regional disparities in OCS use, with a region reaching as high as 21.6 claims per patient. Conclusions: Despite the widespread availability of bx covered by private payers, the majority of patients with uncontrolled SA had no bx claims but substantial OCS use, highlighting a significant gap in the management of SA. This emphasises the need for better education of healthcare providers and improved patient awareness of treatment options. Regional disparities amplify the need for targeted interventions to improve management strategies and reduce OCS use.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".