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Record W4288734691 · doi:10.2500/jfa.2022.4.220002

Variations in protocol development during oral immunotherapy

2022· article· en· W4288734691 on OpenAlexaff
François Graham

Bibliographic record

VenueJournal of Food Allergy · 2022
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsOral immunotherapyProtocol (science)ImmunotherapyMedicineComputer scienceImmunologyImmune systemPathology

Abstract

fetched live from OpenAlex

Oral immunotherapy (OIT) protocols are not standardized, and a wide heterogeneity exists in the literature. OIT protocol variables include the initiation approach (fixed dose versus oral food challenge), buildup speed (slow versus fast), target maintenance dose (low versus high target dose), type of food used, and use of adjuvants among other variables. Most protocols start with an initial escalation day, which is a series of extremely low doses to safely identify the patients who are most allergic, followed by a buildup period over several months to years until the final target maintenance dose is achieved. Doses are generally increased every 1-2 weeks by a factor of 1.25 to 2 and are adapted based on the patient's symptoms. Protocols are increasingly favoring low-maintenance doses over traditional high maintenance doses, although this needs to be discussed and adapted based on the patient's preferences. Accelerated OIT schedules with using a short treatment of omalizumab can be considered in severe food allergy cases.

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)0.175
metaresearch head score (Gemma)0.232
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.175
Threshold uncertainty score0.926

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1750.232
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.004
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0040.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0310.013

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 head0.028
GPT teacher head0.317
Teacher spread0.288 · 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 designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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