ALERT (ChAracterizing uncontroLled sevERe asThma in Canada)
Bibliographic record
Abstract
BACKGROUND: -agonists (SABAs), which are associated with substantial short- and long-term adverse effects. OBJECTIVE: ALERT aimed to characterize the demographics of patients with severe asthma and uncontrolled severe asthma with and/or without biologics in Canada and describe OCS/SABA and biologic treatment patterns. METHODS: ALERT was a retrospective descriptive study using longitudinal claims data from IQVIA's private drug plan database and the Ontario drug benefits database. Adult patients with an inferred asthma diagnosis were assessed and selected using a rule-based inference algorithm and further classified as having severe asthma, uncontrolled severe asthma, and uncontrolled severe asthma without biologics, based on eligibility criteria including inhaled therapies and OCS use. Patients were assessed for OCS, SABA, and biologic use in the 12-month analysis period; regional variation was described. RESULTS: Patients with severe asthma, uncontrolled severe asthma, and uncontrolled severe asthma without biologics had a mean of 2.7, 4.4, and 4.2 OCS claims per patient per year, respectively. Of patients with uncontrolled severe asthma, 8.3% had ≥10 OCS claims. Combined OCS/SABA overuse (≥2 OCS/≥3 SABA claims in the study period) was recorded in 6.1% of patients with severe asthma. Most patients with uncontrolled severe asthma (71.8%) had no biologic claims. Regional disparities in OCS use were observed. CONCLUSION: Optimization of asthma management through improved diagnosis, patient education, earlier specialist referral, and region-specific improvements is needed to reduce OCS/SABA use and increase biologic uptake for eligible patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".