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Record W6931770394 · doi:10.53238/br_20244_509

Food allergies and intolerances in patients with fibromyalgia: the state of the art

2024· article· en· W6931770394 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsFibromyalgiaCulpritAllergyFood allergyFood intoleranceNoceboAsthmaIrritable bowel syndrome

Abstract

fetched live from OpenAlex

The approach to adverse reactions to food represents a difficult task for clinicians due to several unmet needs encompassing all the phases of the medical act. Globally, the prevalence of these reactions shows a progressive increase, but it should be considered that the prevalence of food allergy is largely affected by the diagnostic test used; nonetheless, the lack of accurate tests makes it difficult to calculate the prevalence of food intolerance. In patients suffering from fibromyalgia, food intake is very frequently reported as one of the main responsible for worsening of symptoms, and at diagnosis many patients have already modified their diet, often without a previous nutritional evaluation. Patients frequently start a gluten-free diet and/or a lactose-free diet before the real need is ascertained. Dairy products, incompletely absorbed carbohydrates and gluten-containing foods represent the products the patients consider more frequently the culprit of the onset or worsening of their symptoms. However, the known nocebo effect of foods may induce patients to erroneously associate some foods to symptom onset, also causing a useless elimination diet. The strict association between fibromyalgia and functional gastrointestinal disorders increases the difficulty clinicians have to front during diagnostic approach. Further studies are needed to define the real prevalence of allergy and intolerance in fibromyalgia and to select foods with a negative or positive effect on symptoms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.078
GPT teacher head0.454
Teacher spread0.376 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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