Prevalence of 'novel' food allergens worldwide:: A systematic review
Bibliographic record
Abstract
Rationale: Systematically identifying and reviewing population-based studies reporting the prevalence of food allergy enables geographical variations to be described and helps to identify the prevalence of novel food allergens. Such information can inform evidence-based policy and practice at a national and international level, while also identifying gaps in the current evidence base. Objective: To investigate the prevalence, by age, of food allergy to celery, lupin, mustard and sesame in different regions of the world. This systematic review formed part of a wider investigation on the prevalence of a range of allergen Methodology: Systematic searches were conducted using two databases; Web of Science and Pubmed. Grey literature was identified by searching conference proceedings and by consultation with experts in the field. Search results were managed using reference management software. Titles and abstracts, and the full-text of potentially eligible articles were screened for inclusion by the review authors according to the following criteria: Relevant data was extracted from all included studies, and the quality of included studies assessed. All allergens were initially included, however for the purpose of this study only data relevant to celery, lupin, mustard and sesame are presented Results: CELERY: There were four studies which reported prevalence data for celery in Europe as well as the United States and Australia LUPIN: No studies could be found which reported the prevalence of lupin allergy MUSTARD: There were two studies which reported prevalence data for mustard in Europe and the United States and Australia SESAME: There were 13 studies which reported prevalence date for sesame in Europe as well as Canada, Australia and the United States Conclusions: There is surprisingly little data available on the prevalence of these novel food allergens despite the fact that they appear in the top 14 food allergens listed by the EU. Furthermore, the gold-standard of diagnosis, food challenges, were only adopted in one study which looked at sesame allergy in children. Further studies which confirm allergy based on food challenges are needed to understand the true effect these allergens have on the general population. Since the present review was conducted, we are aware that DBPCFC data for celery allergy has emerged from the EuroPrevall project which reports that 41 out of 64 patients (64.1%) who self-reported an adverse reaction to celery experienced a positive reaction when challenged.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".