MétaCan
Menu
Back to cohort
Record W3195492417 · doi:10.1002/clt2.12046

Peanut allergy: Beyond the oral immunotherapy plateau

2021· review· en· W3195492417 on OpenAlex
Kelly Bruton, Paul Spill, Derek K. Chu, Susan Waserman, Manel Jordana

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical and Translational Allergy · 2021
Typereview
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversityImpactPrograms for Assessment of Technology in Health Research InstituteMcMaster University Medical Centre
Fundersnot available
KeywordsPeanut allergyOral immunotherapyMedicineAllergyImmunotherapyImmunologyAdverse effectDesensitization (medicine)DiseaseFood allergyImmune systemInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There are a lack of disease-modifying treatments for peanut allergy, which is lifelong in most instances. Oral immunotherapy has remained at the forefront of prospective treatments, though its efficacy is consistently undermined by the risk of adverse reactions and meager sustained effects. AIM: This review discusses the current state of oral immunotherapy, its strengths and limitations, and the future of therapeutics for the treatment of peanut allergy. CONCLUSION: The persistence of peanut allergy is currently attributed to reservoirs of peanut-specific memory B cells and Th2 cells, though the cellular and molecular interplay that facilitates the replenishment of peanut-specific IgE remains elusive. Uncovering these events will prove critical for identification of novel targets as we forge ahead to a new age of peanut allergy treatment with biotherapeutics.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.131
GPT teacher head0.435
Teacher spread0.304 · 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