Elucidating the Real‐World Burden of Chronic Rhinosinusitis With Nasal Polyps in Patients in the USA
Bibliographic record
Abstract
Objective: To characterize healthcare burden, treatment patterns, and clinical characteristics associated with chronic rhinosinusitis with nasal polyps (CRSwNP). Study Design: Retrospective cohort. Setting: Real-world study using US health insurance claims database. Methods: Adults with ≥1 CRSwNP diagnosis (index date: first claim for nasal polyps [NPs] between January 1, 2008, and March 31, 2019) and continuous health insurance coverage for ≥180 days preindex (baseline) and postindex were included. Follow-up spanned from index to the earliest of disenrollment, death, or data end. Assessments included patient demographics, comorbidities, and blood eosinophil count at baseline, healthcare resource utilization (HCRU), and costs during follow-up in the overall population and stratified by number of surgeries. Results: Of the 119,357 patients who met the inclusion criteria, 33,748 (28%) had ≥1 surgery during follow-up, among whom 3262 (9.7%) had ≥2 surgeries. At baseline, patients with ≥1 vs no NP surgeries had a greater comorbidity burden; a higher proportion of patients had comorbid asthma (37.8% vs 21.8%) and blood eosinophil count ≥300 cells/µL (42.6% vs 38.1%). During follow-up, patients with NP surgeries had higher all-cause and CRSwNP-related HCRU and costs than patients without NP surgery. All-cause healthcare costs per person per year increased with the number of surgeries during follow-up (no surgery, $10,628; ≥1 surgery, $20,747; ≥2 surgeries, $26,969). Conclusion: Patients with CRSwNP and surgery had a greater disease burden than those without surgery, with higher HCRU and costs, and were more likely to have comorbid conditions (most commonly asthma) and elevated blood eosinophil count, indicating a subset of patients with recalcitrant CRSwNP.
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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 | 0.000 |
| 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".