Diagnostic accuracy, risk assessment, and cost‐effectiveness of component‐resolved diagnostics for food allergy: A systematic review
Bibliographic record
Abstract
BACKGROUND: Component-resolved diagnostics (CRD) are promising tools for diagnosing food allergy, offering the potential to determine specific phenotypes and to develop patient-tailored risk profiles. Nevertheless, the diagnostic accuracy of these tests varies across studies; thus, their clinical utility remains unclear. Therefore, we synthesized the evidence from studies investigating the diagnostic accuracy, risk assessment ability, and cost-effectiveness of CRD for food allergy. METHODS: We systematically searched 10 electronic databases and four clinical trial registries for studies published from January 2000 to February 2017. The quality of included studies was assessed using QUADAS-2. Due to heterogeneity, we narratively synthesized the evidence. RESULTS: Eleven studies met inclusion criteria, altogether recruiting 1098 participants. The food allergies investigated were cow's milk, hen's egg, peanut, hazelnut, and shrimp. The components with the highest diagnostic accuracy for each allergen, along with their sensitivity-specificity pairs, were as follows: Bos d 4 for cow's milk (62.0% and 87.5%), Gal d 1 for hen's egg (84.2% and 89.8% for heated egg, and 60.6% and 97.1% for raw egg), Ara h 6 for peanut (94.9% and 95.1%), Cor a 14 for hazelnut (100% and 93.8%), and Lit v 1 for shrimp (82.8% and 56.3%) allergy. CONCLUSION: Selected components of cow's milk, hen's egg, peanut, hazelnut, and shrimp allergen showed high specificity, but lower sensitivity. However, few studies exist for each component, and studies vary widely regarding the cutoff values used, making it challenging to synthesize findings across studies. Further research is needed to determine clinically appropriate cutoff values, risk assessment abilities, and cost-effectiveness of CRD approaches.
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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 | 0.021 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".