Bibliographic record
Abstract
Recent studies suggest that the prevalence of food allergy is increasing; however, Canadian data on prevalence are sparse. Additionally, although there is unanimous agreement in the medical community that all individuals with a history of food-induced anaphylaxis should have an epinephrine auto-injector (EAI), there is much evidence to suggest that this is not the reality. Using a cross-sectional, randomized telephone survey of Canadian households, we sought to estimate the prevalence of food allergy in Canada and the proportion of allergic Canadians with the EAI, and to determine whether certain characteristics were associated with having the EAI. Of the 10,596 households contacted, 3,666 responded (34.6%), of which 3,613 households, representing 9,667 individuals, provided enough information to be included in the prevalence calculations. The prevalence of self-reported allergy to any food was 8.0%. Of those with probable allergy to at least one of peanut, tree nut, fish, shellfish, and sesame (3.21%), only about 50% had the EAI, and males, those who were older, and those who were single were even less likely to have an EAI. This research suggests that food allergy is a significant health problem, affecting 1 out of every 13 Canadians, and many of them are not adequately managed for their condition. These findings support the need for better education of the public and health care professionals regarding the importance of proper diagnosis and follow-up of individuals with food allergy, and the need to prescribe the EAI to all individuals with a history of an allergic reaction.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".