Prevalence of Allergy in Patients with Chronic Rhinosinusitis
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to provide further evidence that allergic rhinitis is an important factor in chronic and recurrent acute rhinosinusitis. Specifically, this study shows that perennial allergens play a more significant role than seasonal allergens. STUDY DESIGN AND SETTING: Census by chart review of patients with chronic and recurrent acute rhinosinusitis presenting to the Department of Otolaryngology at the MetroHealth Medical Center, Cleveland, OH. METHODS: All participants had allergy testing done either by RAST or intradermal skin endpoint titration utilizing a battery of seasonal and perennial antigens. RESULTS: Of the 48 voluntary participants analyzed in this study, 57.4% had a positive allergy test. Most patients in the study were sensitive to more than one allergen. Of the patients with a positive allergy test, 92% demonstrated sensitivity to one or more perennial allergens-most prominently, molds and dust mites. CONCLUSIONS: Perennial allergy has a statistically significant association with chronic and recurrent acute rhinosinusitis. SIGNIFICANCE: The diagnosis and management of perennial allergies may be beneficial when treating chronic sinus disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".