A review of food allergy-related costs with consideration to clinical and demographic factors
Bibliographic record
Abstract
PURPOSE OF REVIEW: To provide an overview of the magnitude and sources of food allergy-related costs, with a particular emphasis on the recent literature. We also aim to identify clinical and demographic factors associated with differences in food allergy-related costs. RECENT FINDINGS: Recent research has expanded upon previous studies by making greater use of administrative health data and other large sample designs to provide more robust estimates of the financial burden of food allergy on individuals and the healthcare system. These studies shed new light on the role of allergic comorbidities in driving costs, and also on the high costs of acute food allergy care. Although research is still largely limited to a small group of high-income countries, new research from Canada and Australia suggests that the high costs of food allergy extend beyond the United States and Europe. Unfortunately, as a result of these costs, newly emerging research also suggests that individuals managing food allergy, may be left at greater risk of food insecurity. SUMMARY: Findings underscore the importance of continued investment in efforts aimed at reducing the frequency and severity of reactions, as well as programs designed towards helping offset individual/household level costs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".