Hay Rebecca E, Zorko David J, O'Hearn Katie, McQuaid Cara, Thibodeau Geneviève Du Pont, Garcia Guerra Gonzalo, Olivier Jeremy, Ducharme-Crevier Laurence, Lee Laurie, Del Bel Michael J, Choong Karen, McNally James Dayre
Division of Pediatric Critical Care, Department of Pediatrics, University of Ottawa, Ottawa, ON, Canada.
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.
Pediatr Crit Care Med. 2025 Aug 1;26(8):e1017-e1023. doi: 10.1097/PCC.0000000000003780. Epub 2025 Jun 25.
As research examining child health outcomes after PICU admission grows, so does the need for the identification and synthesis of a large body of literature. We aimed to create an open-access scoping repository of literature describing longer-term health outcomes after PICU admission, using a large multinational team (crowdsourcing) and a machine learning (ML) algorithm.
We performed a registered scoping review (OSF DOI10.17605/OSF.IO/HE5VB; Registered November 21, 2022) using MEDLINE, Embase, CINAHL, and CENTRAL databases, 2000-2022, with no language restrictions.
Observational or interventional studies describing outcomes of children (0-17 yr old) and their families or caregivers measured greater than 2 weeks post-PICU discharge. Titles and abstracts and full texts were initially screened by a large team of PICU healthcare workers and researchers who were recruited as part of an Evidence Hackathon event at the 2022 World Federation of Pediatric Intensive and Critical Care Societies conference. Initial screening results from 5000 citations were used to develop and validate an ML algorithm, after which a hybrid human crowdsourcing and ML approach was used to screen the remaining 11,055 studies.
Not applicable.
Of 16,055 eligible citations, 1,301 met the criteria at full text for inclusion in the database. The screening was completed in just under 2 months while adhering to the gold standard systematic review methodology. Sensitivity for the hybrid human crowdsourcing and ML was 98%.
A collaborative, global PICU team integrated with ML was successful in efficient and accurate large data synthesis, producing a scoping open-access database of studies reporting on post-PICU outcomes. The development of this repository has implications for future reviews, providing opportunities for networking and collaborative engagement in research. The next steps should examine database maintenance, utilization, and dissemination of research findings.
随着关于儿科重症监护病房(PICU)收治后儿童健康结局的研究不断增加,识别和综合大量文献的需求也日益增长。我们旨在利用一个大型跨国团队(众包)和机器学习(ML)算法,创建一个开放获取的文献综述库,描述PICU收治后的长期健康结局。
我们使用MEDLINE、Embase、CINAHL和CENTRAL数据库,于2000年至2022年进行了一项注册范围综述(OSF DOI10.17605/OSF.IO/HE5VB;2022年11月21日注册),无语言限制。
描述儿童(0至17岁)及其家庭或照料者结局的观察性或干预性研究,测量时间为PICU出院后超过2周。标题、摘要和全文最初由一大组PICU医护人员和研究人员进行筛选,这些人员是在2022年世界儿科重症和危重症医学会会议的循证黑客马拉松活动中招募的。从5000条引用文献的初步筛选结果用于开发和验证ML算法,之后采用人工众包和ML相结合的方法筛选其余11,055项研究。
不适用。
在16,055条符合条件的引用文献中,有1301条全文符合纳入数据库的标准。筛选在不到2个月的时间内完成,同时遵循了金标准系统综述方法。人工众包和ML相结合方法的灵敏度为98%。
一个与ML整合的协作性全球PICU团队成功地进行了高效且准确的大数据综合,生成了一个关于PICU后结局报告研究的开放获取综述数据库。该数据库的开发对未来的综述有影响,为研究中的网络建设和合作参与提供了机会。下一步应研究数据库的维护、利用以及研究结果的传播。