ArticleEarly human development2023
Enhancing motor screening efficiency: Toward an empirically derived abridged version of the Alberta Infant Motor Scale.
Article in Early human development, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Application of machine learning in early childhood development research: a scoping review.BMJ open · 2025Article
- Clinical validation of an abridged AIMS: Streamlining motor screening in the first-year infant.Early human development · 2025Article
- Defining a universal measurement unit and scale for gross motor development.Frontiers in rehabilitation sciences · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Use of machine learning (ML) in the early detection of developmental delay is possible through the analysis of infant motor skills, though the large number of potential indicators limits the speed at which the system can be trained. Body joint obstructions, the inability to infer aspects of movement such as muscle tone and volition, and the complexities of the home environment - confound machine learning's ability to distinguish between some motor items. To train the system efficiently requires using an excerpted list of validated items, a salient set, which uses only those motor items that are the 'easiest' to see and identify, while being the most highly correlated to a low/qualifying score. This work describes the examination of motor items, selection of 15 items that comprise the salient set, and the ability of the set to reliably screen for motor delay in the first-year infant.
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