Advances in biologic therapies for chronic rhinosinusitis with nasal polyps
Bibliographic record
Abstract
Chronic rhinosinusitis (CRS) is a complex multifactorial inflammatory disease that affects more than ten percent of the adult population globally. CRS represents a large healthcare burden and is associated with significant morbidity. Despite conventional medical and surgical treatments, a subset of patients continue to have poor symptom control due to substantial inflammatory disease persistence. These difficult-to-treat patients represent ideal candidates for biologic therapeutics, which target key inflammatory processes implicated in CRS disease. Biologic agents targeting type 2 inflammation have been shown to reduce disease burden. Phase 3 clinical trials studying the effects of anti-IgE, anti-IL-5/anti-IL-5Rα, and anti-IL-4/IL-13 humanized monoclonal antibodies have shown the efficacy of these therapies to reduce polyp size, the need for revision surgeries, improve daily symptoms, and downregulate inflammatory markers while maintaining an acceptable safety profile. The reductions seen in inflammatory mediators are largely transient following treatment termination and additional investigations are required to discern the long-term effects of biologic use on disease modulation. Specifically, the evidence suggests that these biologics are beneficial for patients with CRSwNPs with comorbid asthma and aspirin exacerbated respiratory disease (AERDBiologics should be used sparingly because of significant cost and accessibility of the treatment. Current guidelines recommend that biologics be reserved for patients with CRSwNP with moderate to severe disease who have failed conventional medical and surgical therapy. Further studies are needed to better endotype patients to optimize biologic use, evaluate long-term effectiveness and compare the relative effectiveness of biologic therapies in these difficult-to-treat patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".