Global Variability in Administrative Approval Prescription Criteria for Biologic Therapy in Severe Asthma
Bibliographic record
Abstract
BACKGROUND: Regulatory bodies have approved five biologics for severe asthma. However, regional differences in accessibility may limit the global potential for personalized medicine. OBJECTIVE: To compare global differences in ease of access to biologics. METHODS: In April 2021, national prescription criteria for omalizumab, mepolizumab, reslizumab, benralizumab, and dupilumab were reviewed by severe asthma experts collaborating in the International Severe Asthma Registry. Outcomes (per country, per biologic) were (1) country-specific prescription criteria and (2) development of the Biologic Accessibility Score (BACS). The BACS composite score incorporates 10 prescription criteria, each with a maximum score of 10 points. Referenced to European Medicines Agency marketing authorization specifications, a higher score reflects easier access. RESULTS: Biologic prescription criteria differed substantially across 28 countries from five continents. Blood eosinophil count thresholds (usually ≥300 cells/μL) and exacerbations were key requirements for anti-IgE/anti-IL-5/5R prescriptions in around 80% of licensed countries. Most countries (40% for dupilumab to 54% for mepolizumab) require two or more moderate or severe exacerbations, whereas numbers ranged from none to four. Moreover, 0% (for reslizumab) to 21% (for omalizumab) of countries required long-term oral corticosteroid use. The BACS highlighted marked between-country differences in ease of access. For omalizumab, mepolizumab, benralizumab, and dupilumab, only two, one, four, and seven countries, respectively, scored equal or higher than the European Medicines Agency reference BACS. For reslizumab, all countries scored lower. CONCLUSIONS: Although some differences were expected in country-specific biologic prescription criteria and ease of access, the substantial differences found in the current study present a challenge to implementing precision medicine across the world.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".