Efficacy of Omalizumab against Japanese Cedar pollinosis in clinical practice
Bibliographic record
Abstract
BACKGROUND: Japanese cedar pollinosis (JCP), which affects over 40% of the population and represents a major public health issue in Japan, has various treatment options, but limited clinical reports and high costs necessitate careful patient selection. This study aimed to evaluate the efficacy of omalizumab in treating JCP and identify key considerations for its appropriate clinical application. METHODS: We retrospectively analyzed 42 patients with JCP treated with omalizumab from 2021 to 2024. Treatment response was assessed using a 3-category patient-reported scale across all years. In a 2023-2024 subset (n = 23), quantitative total symptom score (TSS) data were available, allowing effect-size estimation and subgroup analyses. RESULTS: The study included 42 patients (30 men, 71.4%) aged 12-80 years (mean, 41.5 ± 17.2 years). Symptom improvement was observed in 38 patients (90%), including marked improvement in 23 (55%). Responders were younger (mean age 39.9 vs. 55.5 years) and had higher total IgE levels (251 vs. 211 IU/mL) than nonresponders. No significant correlation was observed between nasal eosinophil counts and treatment response. In the 2023-2024 subset (n = 23), TSS decreased significantly (mean paired difference - 2.61; p = 0.0010). Subgroup analyses suggested that CRSsNP cases tended to show insufficient improvement, while younger age and higher IgE levels were associated with better response trends. CONCLUSIONS: Omalizumab significantly improved symptoms and QOL in patients with JCP. However, its high cost and the risk of nonresponse necessitate careful patient selection. Age and IgE levels may help guide treatment decisions, highlighting the importance of individualized strategies for severe JCP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".