Food allergies and intolerances in patients with fibromyalgia: the state of the art
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".