Updated perspective on the development of food allergy in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rising prevalence of food allergy is a global concern, especially as children are more susceptible to developing anaphylaxis than adults. Food-induced anaphylaxis, a severe and potentially life-threatening allergic reaction, imposes a healthcare burden in many Asian countries. While literature on food allergy in Asia is limited and heterogeneous, the prevalence shows an upward trend. In contrast, food allergy in developing countries is often overlooked as a health issue. China, with its vast population and landmass, is experiencing notable shifts in food allergy patterns. Unlike Western countries where tree nuts are common triggers, wheat, seafood, and fruits increasingly provoke allergic reactions among the Chinese population. Various diagnostic methods for food allergy were employed in China; however, the lack of a standardized approach presents challenges for effective management. In the future, it is essential to develop efficient and convenient detection methods while utilizing big data for extensive epidemiological investigations and clinical studies to address the complex health issue of food allergy.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 it