Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen · 2026
[Allergy Prevention L·OVE: Development of a structured, publicly accessible evidence repository for allergy prevention].
Аннотация на английскомСтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Nunner Robert, Veloso Valentina, Ramos-Rojas José, Karki Sudip, Wollmann Michelle, Shin Jaekyung, Sprenger Antonia, Kuper Paula, Hasenpusch Claudia, Skiba Eva-Maria, Sieferle Katharina, Ávila-Oliver Camila, Rada Gabriel, Apfelbacher Christian
Аннотация
BACKGROUND: The prevention of allergic diseases has been addressed by numerous systematic reviews. However, the evidence landscape is fragmented due to heterogeneous populations, interventions, outcomes, and dynamically evolving research approaches. Structured and continuously updated evidence resources are therefore needed to support guideline development, healthcare decision-making, and public health. Against this background, the Allergy Prevention Living Overview of Evidence (L·OVE) was developed as a freely accessible, continuously updated evidence repository for allergy prevention. The aim of this study is to describe the conceptual and methodological development of the repository as well as selected preliminary findings.
METHODS: A systematic search was conducted in the Epistemonikos database for systematic reviews. In order to structure the evidence, a hierarchical taxonomy was developed covering the dimensions population, intervention, contextual dimensions, and outcome. Systematic reviews on primary preventive interventions aiming to prevent the development of allergic diseases or allergic sensitization were included. Relevant evidence was identified using an automated, platform-supported classification and identification workflow validated through a relative recall approach; included reviews were subsequently screened and taxonomically classified manually by two independent reviewers. Disagreements were resolved through discussion and, if necessary, by consultation with a third reviewer.
RESULTS: As of 18 November 2025, the repository included 245 systematic reviews that met the inclusion criteria after a systematic search and multi-stage selection process. Nutrition- and microbiome-related interventions dominated the available evidence base, whereas nature-based, behavioral, and environmentally oriented prevention strategies were comparatively rarely addressed in systematic reviews. The automated, platform-supported identification strategy achieved a sensitivity of 96.49% compared with 92.98% for a Boolean search strategy, while also reducing the manual screening workload.
DISCUSSION: The findings indicate an uneven distribution of systematically synthesized evidence across different prevention domains and highlight potential research gaps. At the same time, the approach shows that systematic reviews in allergy prevention can be organized and continuously updated using structured taxonomies and automated, platform-supported identification procedures. The repository does not perform evidence synthesis itself, but rather supports the structured retrieval, classification, and contextualization of existing evidence.
CONCLUSION: The Allergy Prevention L·OVE provides a structured, freely accessible, and continuously updatable evidence repository for allergy prevention. By combining automated evidence identification, a structured taxonomy, and expert curation, it can support researchers, guideline groups, and stakeholders in healthcare and public health in identifying systematic reviews more efficiently, recognizing evidence gaps, and making more transparent use of the existing evidence base.
METHODS: A systematic search was conducted in the Epistemonikos database for systematic reviews. In order to structure the evidence, a hierarchical taxonomy was developed covering the dimensions population, intervention, contextual dimensions, and outcome. Systematic reviews on primary preventive interventions aiming to prevent the development of allergic diseases or allergic sensitization were included. Relevant evidence was identified using an automated, platform-supported classification and identification workflow validated through a relative recall approach; included reviews were subsequently screened and taxonomically classified manually by two independent reviewers. Disagreements were resolved through discussion and, if necessary, by consultation with a third reviewer.
RESULTS: As of 18 November 2025, the repository included 245 systematic reviews that met the inclusion criteria after a systematic search and multi-stage selection process. Nutrition- and microbiome-related interventions dominated the available evidence base, whereas nature-based, behavioral, and environmentally oriented prevention strategies were comparatively rarely addressed in systematic reviews. The automated, platform-supported identification strategy achieved a sensitivity of 96.49% compared with 92.98% for a Boolean search strategy, while also reducing the manual screening workload.
DISCUSSION: The findings indicate an uneven distribution of systematically synthesized evidence across different prevention domains and highlight potential research gaps. At the same time, the approach shows that systematic reviews in allergy prevention can be organized and continuously updated using structured taxonomies and automated, platform-supported identification procedures. The repository does not perform evidence synthesis itself, but rather supports the structured retrieval, classification, and contextualization of existing evidence.
CONCLUSION: The Allergy Prevention L·OVE provides a structured, freely accessible, and continuously updatable evidence repository for allergy prevention. By combining automated evidence identification, a structured taxonomy, and expert curation, it can support researchers, guideline groups, and stakeholders in healthcare and public health in identifying systematic reviews more efficiently, recognizing evidence gaps, and making more transparent use of the existing evidence base.