Self-reported questionnaire survey on the prevalence and symptoms of adverse food reactions in patients with chronic inhalant diseases in Tangshan city, China
Bibliographic record
Abstract
BACKGROUND: The prevalence of adverse food reactions in patients with chronic inhalant diseases has seldom been studied in China. This study is to investigate the prevalence of adverse food reactions and the symptoms caused in respiratory patients. METHODS: Respiratory patients in allergy clinics were asked to complete a questionnaire. Patients' information such as age, gender, family history of allergy, and adverse reactions to a list of 48 foods and the symptoms caused, was recorded. Multivariate analyses were performed to determine the prevalence of adverse food reactions and their associated symptoms. RESULTS: 459 subjects, with an average age of 32 years old, completed the questionnaire; 45.3% were male. Among the 459 subjects, 38.1% (175/459) had an adverse reaction to food: 13.6% had an adverse food reaction to crab, 12.4% had an adverse food reaction to shrimp; and 9.9% had an adverse reaction to shellfish. Peach and nectarine were also shown to be common causative foods with 6.8% of the study group showing an adverse reaction to peach and 5.2% to nectarine. Seafood mainly caused skin symptoms and fruits gave rise to more throat, oral, and gastrointestinal problems. CONCLUSION: The prevalence of adverse food reactions is high for patients with respiratory diseases. This indicates that adverse food reactions should be considered in the treatment and management of patients with chronic inhalant diseases.
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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 | 0.001 |
| 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) | 0.001 | 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".