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Record W2272308722 · doi:10.1159/000443764

How Low Should We Go?

2015· letter· en· W2272308722 on OpenAlexaff
Julia Upton, Thomas Eiwegger

Bibliographic record

VenueInternational Archives of Allergy and Immunology · 2015
Typeletter
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineIngestionAllergyOral food challengeFood allergyImmunotherapyPopulationMaintenance doseMilk allergyPeanut allergyImmunologyImmune systemInternal medicine

Abstract

fetched live from OpenAlex

peanut protein without symptoms [5] . Blumchen et al. [6] targeted 500 mg of whole crushed peanuts, corresponding to 125 mg peanut protein, and found that some participants tolerated 4 g of crushed peanuts. Therefore, it is documented that participants could tolerate more than 10× the dose used for maintenance OIT. Other methodologies of immunotherapy which target lower maintenance doses than many OIT protocols include sublingual immunotherapy to milk and epicutaneous immunotherapy [reviewed in 2 ] ( fig. 1 ). If ingestion of a very low amount of food can elicit a clinical reaction, then perhaps very low doses can also be immune modifying. A review of published trials found that the minimum eliciting dose was 3.3 mg for milk [7] . This amount would correspond to about 0.009 ml or less than 1⁄4 of a drop of milk. In a recent edition of the International Archives of Allergy and Immunology , Yanagida et al. [8] explore the use of an LOIT protocol to milk in a severely milk-allergic population. The maintenance dose target was only 3 ml of milk or just over 100 mg of milk protein. This dose is less than 2% of what would traditionally be considered a serving of milk. They used an initial 5-day in-hospital dose escalation protocol with ongoing daily doses and any required dose escalation was at home. Of the 12 patients on treatment, 9/12 could drink 3 ml of milk daily without reactions and 4/12 could tolerate 25 ml, which Milk allergy is a common phenomenon with a prevalence of around 2% in 2-year-old children. Avoidance itself is not sufficient to prevent allergic reactions as suggested by one study reporting an incidence of 40% allergic reactions during a 12-month period. Thirty-seven percent of reactions were moderate to severe [1] . Therefore feasible treatment options for milk allergy are needed. Milk oral immunotherapy (OIT) has been investigated for treatment of milk allergy [2] . Many OIT trials targeted serving-size amounts of food such as approximately 5– 16 g in milk, 2 g in egg and 0.8–7 g in peanut OIT trials. Initial enthusiasm over OIT to milk was tempered by allergic reactions (19% with anaphylactic reaction at least once) and by limited long-term responders (31%) [3] . The OIT method of the recent long-term follow-up study of milk used 500 mg milk protein and had a median maximum tolerated dose after OIT of 1,400 mg [3] . Previous studies in peanut have shown that low-dose OIT (LOIT) may have promise as a treatment. Recently, an abstract has attracted attention at the AAAAI [4] . In this study, yet to be reported unblinded, it appeared that there was little difference in clinical and immunological outcomes in young children randomized to low-dose (300 mg) or high-dose (3,000 mg) OIT to peanut. Previous to that study, the same center initially targeted a maintenance dose of 300 mg of peanut protein and 93% who completed the protocol passed a challenge of 3,900 mg Published online: January 21, 2016

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.053
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0050.010
Open science0.0020.003
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0530.028

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.

Opus teacher head0.032
GPT teacher head0.289
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations4
Published2015
Admission routes1
Has abstractyes

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