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Record W4408247030 · doi:10.1080/1744666x.2025.2475984

French Guiana and Alpha-Gal syndrome: a path to uncovering hidden clues

2025· review· en· W4408247030 on OpenAlexaff
Evrard Baduel, M. Smilov, Loïc Epelboin

Bibliographic record

VenueExpert Review of Clinical Immunology · 2025
Typereview
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsCegep de Saint Hyacinthe
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

INTRODUCTION: This article is a narrative review exploring how research in French Guiana could unlock the mysteries surrounding Alpha-Gal Syndrome (AGS), a recently identified IgE-mediated allergy with delayed reactions to exposure to non-Catarrhine mammalian-derived products. Although fewer than 10 cases have been reported across Latin America, two case series involving 11 and 18 patients with AGS have been documented in French Guiana. AREAS COVERED: This article discusses risk factors such as ethnicity, prior pathogen-induced immunization to α-Gal, vectors responsible for AGS, their environment and ecosystems, observed phenotypes, and therapeutic implications for sensitized individuals. Literature research was based on PubMed between 12/2023 and 08/2024, using: α-Gal/Alpha-1,3-Galactose/galactose-α 1,3-galactose/Red meat allergy/Mammalian meat allergy/Alpha gal syndrome/Antivipmyn. Grey literature from French Guiana were obtained from Prof. Loïc Epelboin. EXPERT OPINION: Advancing AGS research in French Guiana could yield valuable epidemiological insights, as existing data predominantly stem from European, North American, Australian, and Japanese contexts - regions with comparatively lower diversity in tick species, their mammalian hosts, associated pathogens, and parasitic infestations. Additionally, French Guiana presents unique therapeutic scenarios, such as Viperidae envenomation and transfusions under inventory constraints, that merit further investigation.

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 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)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.487
Teacher spread0.410 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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