The use of grocery-sourced real-food solutions in sublingual immunotherapy for food allergies
Bibliographic record
Abstract
BACKGROUND: Sublingual immunotherapy (SLIT) is a safe, effective therapy for the treatment of food allergy. Studies demonstrating SLIT efficacy have primarily used pharmaceutical glycerinated food extracts for the administration of food allergens, which may limit accessibility due to extract cost and availability. OBJECTIVE: To develop novel sample protocols and resources for the preparation of grocery-sourced real-food SLIT solutions, which could help more clinicians incorporate food SLIT into their practice and increase accessibility to this treatment. Second, to describe our site's experience with real-food SLIT implementation. METHODS: Three- and five-dose build-up protocols were developed using powdered- or liquid-based forms of food allergens, with a maintenance dose of 2 to 4 mg protein/d. Patient adherence and satisfaction data were collected through online surveys. After 1 to 2 years of daily real-food SLIT maintenance dosing, patients were offered a low-dose oral food challenge (cumulative dose, 330-340 mg protein). RESULTS: Sample protocols for real-food SLIT were developed for 31 foods, including peanut, cow's milk, cashew, egg, and sesame. At our site, 305 patients have undergone or are currently undergoing real-food SLIT. Of 162 satisfaction survey respondents, 99% (n = 160) were satisfied or very satisfied with their care. Adherence surveys revealed that 82% of the respondents (n = 105/128) reported consistently taking their SLIT dose. Among a subset of 33 patients, 57 low-dose oral food challenges were performed, of which 70.1% (n = 40) were successful. CONCLUSION: Grocery-sourced real-food SLIT solutions present another food SLIT option that may expand the feasibility and accessibility of this safe and effective food allergy immunotherapy.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".