Successful prevention of extremely frequent and severe food anaphylaxis in three children by combined traditional Chinese medicine therapy
Bibliographic record
Abstract
BACKGROUND: Despite strict avoidance, severely food-allergic children experience frequent and potentially severe food-induced anaphylaxis (FSFA). There are no accepted preventive interventions for FSFA. A Traditional Chinese Medicine (TCM) formula prevents anaphylaxis in murine food allergy models, and has immunomodulatory effects in humans. We analyzed the effects of TCM treatment on three pediatric patients with FSFA. CASE DESCRIPTION: Three FSFA patients (P) ages 9-16 years (P1 allergic to milk; P2 and P3 to tree nuts) qualified for case analysis. All experienced numerous reactions requiring administration of rescue medications and emergency room (ER) visits during the 2 years prior to starting TCM. P1 experienced approximately 100 reactions, 50 epinephrine administrations, 40 ER visits, and 3 admissions to intensive care units. P2 experienced 30 reactions, all requiring epinephrine administration, as well as 10 emergency hospitalizations. P3 experienced 400 reactions, five of which required epinephrine administration and ER visits. TCM treatment markedly reduced or eliminated reactions in all. P1 experienced no reactions after 2.5 years of TCM. P2 experienced no reactions after 1 year of TCM treatment, at which time she passed an oral almond food challenge. She continues to be reaction-free 6 months off TCM while consuming nuts. P3 has achieved a 94% reduction in reaction frequency following 7 months of TCM, has discontinued daily antihistamine use, and has required no epinephrine administrations or ER visits. CONCLUSIONS: Three children treated with TCM experienced dramatic reductions or elimination of FSFA. This regimen appears to present a potential option for FSFA, and warrants further investigation in controlled clinical studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".