Effect of exclusion of frequently consumed dietary triggers in a cohort of children with chronic primary headache
Bibliographic record
Abstract
BACKGROUND: Although dietary factors are known to trigger headaches, the relationship between food and headache in children remains unclear. This prospective, observational case series aimed to evaluate the effect of exclusion of frequently-consumed foods in a cohort of children with headache. METHODS: One hundred and fifteen children aged 3-15 (mean 10.5) years with primary headache were followed in a paediatric outpatient clinic. Patients who frequently consumed foods or food additives known to trigger headaches were advised to exclude them for six weeks and to return for follow-up with headache and food diary. RESULTS: One hundred patients attended follow-up. Of these 13 (13%) did not respond to dietary exclusion; 87 (87%) achieved complete resolution of headaches by exclusion of 1-3 of the identified food(s). Caffeine was the most common implicated trigger (28), followed by monosodium glutamate (25), cocoa (22), aspartame (13), cheese (13), citrus (10) and nitrites (six). One patient was sensitive to tomatoes. CONCLUSIONS: This study demonstrates the potential scale and significance of seven frequently consumed foods or food additives as triggers for primary headache in children. Also this is the first study to show that headaches can be triggered by the cumulative effect of a food that is frequently consumed, rather than by single time ingestion.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".