Physician's appraisal vs documented signs and symptoms in the interpretation of food challenge tests: The EuroPrevall birth cohort
Bibliographic record
Abstract
BACKGROUND: Blinded food challenges are considered the current gold standard for the diagnosis of food allergies. We used data from a pan-European multicenter project to assess differences between study centers, aiming to identify the impact of subjective aspects for the interpretation of oral food challenges. METHODS: Nine study centers of the EuroPrevall birth cohort study about food allergy recruited 12 049 newborns and followed them for up to 30 months in regular intervals. Intensive training was conducted and every center visited to ensure similar handling of the protocols. Suspected food allergy was clinically evaluated by double-blind, placebo-controlled food challenges using a nine dose escalation protocol. The primary challenge outcomes based on physician's appraisal were compared to documented signs and symptoms. RESULTS: Of 839 challenges conducted, study centers confirmed food allergy in 15.6% to 53.6% of locally conducted challenges. Centers reported 0 to 16 positive placebo challenges. Worsening of eczema was the most common sign when challenged with placebo. Agreement between documented objective signs and the challenge outcome assigned by the physician was heterogeneous, with Cohen's kappa spanning from 0.42 to 0.84. CONCLUSIONS: These differences suggest that the comparison of food challenge outcomes between centers is difficult despite common protocols and training. We recommend detailed symptom assessment and documentation as well as objective sign-based challenge outcome algorithms to assure accuracy and comparability of blinded food challenges. Training and supervision of staff conducting food challenges is a mandatory component of reliable outcome data.
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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 | 0.040 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".