Trial reportBMC medical informatics and decision making2025
Assessing pediatric clinician adherence to the guidelines for prevention of peanut allergy: a natural language processing study.
Trial report in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04604431 (Intervention to Reduce Early), which is not on this map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Intervention to Reduce Early (Peanut) Allergy in Children
Who cites it
1 citing paper in PubMed.
- Improving machine-learning development in allergology: bridging the gap between open-access and cohort-based databases.Current opinion in allergy and clinical immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly introduction of peanut products to infants around 4- to 6- months of age may reduce peanut allergy incidence. However, clinician adherence to the National Institute of Allergy and Infectious Diseases’ 2017 Addendum Guidelines for the Prevention of Peanut Allergy (PPA), which recommend early peanut introduction based on risk levels, has been reportedly low. Documentation of clinician peanut introduction recommendations and peanut allergy risk in electronic health records (EHR) is variable and often in the form of unstructured data. Therefore, this study aims to develop and validate a Natural Language Processing (NLP) approach to identify and measure pediatric clinicians’ adherence to the PPA guidelines (peanut introduction recommendations and peanut allergy risk assessments, including eczema severity), as documented in EHR systems.
methodsAn NLP pipeline was developed to process clinical notes and patient instructions from EHRs in the Intervention to Reduce Early Peanut Allergy in Children (iREACH) trial. iREACH is a two-arm, cluster-randomized, controlled clinical trial that evaluates an intervention to enhance clinician adherence to the PPA guidelines. The database includes 4- and 6-month well-child care visits from 30 practices across three clinical networks in Illinois. The development of the NLP was organized into three main phases: exploratory (reviewed EHR notes to identify concepts for developing NLP algorithms), training (resulting in the first version of the NLP algorithm), and validation (based on gold standard datasets). Chart reviews were conducted to review the accuracy and reliability of the NLP model. The NLP pipeline assessed peanut introduction recommendations and severe eczema.
resultsNLP achieved high precision (0.98), recall (0.94) and overall performance F-measure (0.96) for identifying peanut introduction recommendations across the three networks. However, identifying severe eczema proved more challenging, with precision of 0.52, recall of 0.92, and overall performance F-measure of 0.67 across the three networks. Therefore, manual review was used to confirm the severe eczema results for the trial.
conclusionsConsidering the pragmatic study design, clinical notes were the only feasible source of data, yet documentation of severe eczema varied considerably across networks. Future refinement and validation of the severe eczema NLP pipeline are required. NLP is a valuable tool in assessing large EHR data sets in pragmatic, multi-site clinical trials.
trial registrationThis trial is registered on ClinicalTrials.gov (NCT04604431). Registered on October 14, 2020.
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