Do biologics improve health outcomes in children with severe asthma? – A longitudinal cohort study
Bibliographic record
Abstract
Introduction: Monoclonal antibody therapies (“biologics”) target inflammatory mediators in patients with severe asthma but are cost-prohibitive (≈$50,000/patient/year). Comparing health outcomes (effectiveness and safety) in children with severe asthma pre and post biologics will identify the best populations for use. Methods: We conducted a retrospective cohort study using the Observational Medical Outcomes Partnership (OMOP) Common Data Model system, a registry of electronic health records. We included children (12-18y) with confirmed Global Initiative for Asthma diagnosis of severe asthma, using any combination of biologics (omalizumab [OM], mepolizumab [ME], or dupilumab [DU]), treated at the Hospital for Sick Children (Toronto) from Jan 2014-Aug 2023. Health outcomes pre and post biologic (eg. emergency department [ED] visits, admissions) were compared. Results: We identified 16,187 asthma encounters across 5,889 unique patients with severe asthma. Forty-four (0.73%) patients were prescribed a unique biologic (DU n=14; OM n=29; ME n=5), of which 19 (44%) used with oral and 33 (77%) used with inhaled corticosteroids. Post biologics, mean ED visits and hospitalizations per patient decreased from 1.53 to 0.33 and 1.05 to 0.19, respectively (p<0.001). ED visits per patient person-year (PYR) declined from 1.57 to 0.27 (p<0.001), an incidence rate ratio (IRR) of 6.1 (95% CI 3.5-11.8; p<0.0001). Similarly, hospitalizations per patient PYR declined from 1.47 to 0.14 (p<0.001), an IRR of 10.4 (95% CI 4.9-25.6; p<0.0001). Conclusion: Children with severe asthma treated with biologics improved health outcomes, demonstrating effectiveness and potential in decreasing healthcare utilization.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".