Prevalence of Laryngeal and Pharyngeal Symptoms in Patients with Environmental Allergy
Bibliographic record
Abstract
ABSTRACT Background The effect of environmental allergy on laryngeal and pharyngeal complaints is not well understood. The goal of this study was to determine the prevalence of laryngeal and pharyngeal symptoms in patients with environmental inhalant allergy. Methods A retrospective review was conducted of all patients undergoing allergy skin testing between 2006 and 2009. Patients with positive skin tests were included and were classified as ‘mild’ or ‘extensive’ reactors based on skin testing results. Data regarding laryngeal and pharyngeal complaints, as well as ocular and nasal symptoms, had been systematically documented for all patients. The prevalence of these complaints was calculated in each group of reactors and overall. Results The prevalence of laryngeal and pharyngeal complaints was 42.0% overall, 45.5% among the mild reactors, and 39.3% in the extensive reactors. No patients complained of hoarseness or were felt to have dysphonia. There was no statistically significant difference between the mild and extensive reactors (p = 0.78). The prevalence of ocular and/or nasal symptoms was 96.0, 90.9 and 100%, respectively. Conclusion The prevalence of laryngeal and pharyngeal complaints in allergy sufferers was low in this study. Given the conflicting literature on this topic, there is insufficient evidence to establish a causal link between environmental allergy and laryngeal and pharyngeal symptoms. How to cite this article Shah MD, Wise SK, Rotella MR, Ingley AP, Johns MM. Prevalence of Laryngeal and Pharyngeal Symptoms in Patients with Environmental Allergy. Int J Phonosurg Laryngol 2014;4(1):1-4.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".