Real-world severe asthma biologic administration and adherence differs by biologic
Bibliographic record
Abstract
BACKGROUND: Patient adherence to biologic therapies is crucial for clinical benefits. Previous assessments of US patient adherence to severe asthma (SA) biologic therapies have relied on health care insurance claims data that have limitations. OBJECTIVE: To describe real-world, specialist-reported, biologic administration and adherence among US adults with SA. METHODS: CHRONICLE (ClinicalTrials.gov identifier: NCT03373045) is an ongoing real-world, noninterventional study of patients with SA treated by US subspecialists. Sites report date and location for all biologic administrations. We evaluated biologic (benralizumab, dupilumab, mepolizumab, omalizumab, reslizumab) adherence as the proportion of days covered (PDC) during the first 52 weeks and the mean number of days until patients received the expected number of doses for 13, 26, and 52 weeks of treatment. RESULTS: A total of 2117 patients received biologic administrations between February 2018 and February 2022. Most patients (84%) received biologic administrations at a subspecialist site. Over time, administrations at specialist sites decreased, whereas at-home administrations increased. The median PDC was 87%; the mean number of days to receive a 52-week (364-day) equivalent number of doses was 423 for all biologics (average delay of 58 days). Dupilumab had the lowest PDC and highest mean delays in dosing across all intervals; better adherence was observed among commercially insured patients. CONCLUSION: Patients with SA are mostly adherent to biologic therapies. Biologics with shorter dosing intervals and at-home administration had worse adherence, likely because of greater opportunities for delays. Specialist-reported administration data provide a unique perspective on biologic adherence, which may be overestimated for at-home administrations by insurance claims data. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov: NCT03373045.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".