Biologic use and treatment patterns in patients with chronic rhinosinusitis with nasal polyps: a US real-world study
Bibliographic record
Abstract
BACKGROUND: Several biologics are now approved in the US as add-on treatments for chronic rhinosinusitus with nasal polyps (CRSwNP). This cross-sectional, retrospective, real-world study aimed to characterize treatment patterns and identify predictors of biologic use among patients with CRSwNP. METHODS: Adults in the Merative MarketScan Commercial and Medicare Supplemental Databases with medical claims for CRSwNP were identified June 2018-June 2019 (identification period [IP]). Patient characteristics were collated in the IP and treatment pattern data during the IP plus the following year (July 2019-June 2020; observation period [OP]). Data were stratified by sinus surgery and biologic use. RESULTS: Of the 5997 eligible patients identified (58% male, mean age 48.1 years), 10.7% (n = 642) used biologics during the OP. More biologic users had common respiratory conditions than non-users, particularly asthma (89.1% vs 35.0%; P < 0.001). Biologic users had fewer diagnostic services but more drug-related services than non-users. Only 11.6% of patients who had sinus surgery used biologics, with most (56.1%) having their first biologic dose before sinus surgery and 12.5% ≤ 30 days after. Oral corticosteroid (OCS) use was higher in biologic users than non-users (all patients: 68.8% vs 42.5%; P < 0.001) and in those with/without sinus surgery. Comorbidities, prior OCS/doxycycline use, and age (< 65 years) increased the odds of biologic use, with asthma increasing the odds 5.46 times (P < 0.001). CONCLUSIONS: Biologic use was more common before first/next sinus surgery and in patients with high unmet need, elucidating predictors of biologic use that could be used in clinical practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".