计算机算法在识别具有非典型生长模式的瑞典儿童方面表现有前途
Lars Gelander1, Aimon Niklasson1, Anton Holmgren1,2
1Göteborg Pediatric Growth Research Centre, Department of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden.
Acta paediatrica (Oslo, Norway : 1992)
|July 1, 2025
概括
芬兰的生长评估算法有效地识别了瑞典儿童的非典型生长模式. 这些计算机化方法为监测儿童发育和早期识别潜在的健康问题提供了有价值的工具.
科学领域:
- 儿科 儿科 儿科
- 公共卫生 公共卫生
- 生物统计学 生物统计学
背景情况:
- 准确的成长评估对于监测儿童发育至关重要.
- 对比不同的增长评估标准对于优化查协议至关重要.
- 国际肥胖问题工作组 (IOTF) 提供全球增长参考.
研究的目的:
- 将芬兰增长评估算法的性能与瑞典标准和IOTF标准进行比较.
- 评估芬兰算法在识别瑞典儿童非典型生长模式方面的有效性.
主要方法:
- 利用了3,214名年龄在4,6和8岁的瑞典儿童的历史数据.
- 对比了五个芬兰查参数,包括身高SDS和BMI-SDS与瑞典和IOTF参考.
- 分析了瑞典,芬兰和IOTF增长参考的BMI截止值.
主要成果:
- 芬兰的算法确定了高达1.8%的儿童具有低身高或高身高SDS,并随着时间的推移注意到了变化.
- 与IOTF测量相比,芬兰的算法检测到体重不足,超重或肥胖的儿童数量增加了26.4%.
- 根据瑞典的标准,较高比例的矮 (13.6-17.4%) 和高 (15.5-18.6%) 儿童被发现,大多数非典型病例在4岁时被发现.
结论:
- 计算机化的芬兰算法证明了识别异常生长的瑞典儿童的能力.
- 该研究强调了芬兰查参数在儿科生长监测国际应用中的潜力.
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