House dust mite sensitization is the main risk factor for the increase in prevalence of wheeze in 13‐ to 14‐year‐old schoolchildren in Guangzhou city, China
Bibliographic record
Abstract
BACKGROUND: Little is known about time trend of prevalence of asthma and the association between the changing prevalence and allergen sensitization in Chinese children. OBJECTIVE: To determine the changes in prevalence of asthma and allergen sensitization in schoolchildren over a period of 15 years. METHODS: A total of 6928 schoolchildren aged 13-14 years in 2009 were recruited for the study using the Phase III Protocol of the International Study of Asthma and Allergic disease in Childhood (ISAAC) and 2531 of them underwent skin prick test for seven common aeroallergens. The results were compared with those obtained in the Phase I (1994/95) and III (2001/02) ISAAC studies. RESULTS: The prevalence of asthma ever and current wheeze increased from 3.9% and 3.4% in 1994, to 4.6% and 4.8% in 2001 (P<0.001), and to 6.9% and 6.1% in 2009 (P ≤ 0.008). The prevalence of higher degree of skin response to house dust mites (HDMs) and cat, and atopic index increased significantly in all children in 2010 when compared with those in 2002 (P<0.001). Prevalence of wheeze remained unchanged in subjects without sensitization to any tested allergen including HDMs (P > 0.05). Sensitization to HDMs, especially Dermatophagoides Pteronyssinus was associated with increase in prevalence of wheeze. CONCLUSION: The prevalence of wheeze and sensitization to common aeroallergens in secondary schoolchildren in Guangzhou China has increased significantly since 1994. Sensitization to HDMs is an important risk factor associated with the increase in prevalence of wheeze in this group of population in Guangzhou city.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".