ArticleData in brief2022
Loading characteristics data applied on osseointegrated implant by transfemoral bone-anchored prostheses fitted with state-of-the-art components during daily activities.
Article in Data in brief, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Evidence-based recommendation of a powered knee for transfemoral bone-anchored prostheses: A cross-sectional study.Canadian prosthetics & orthotics journal · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The data in this paper are related to the research article entitled "Load applied on osseointegrated implant by transfemoral bone-anchored prostheses fitted with state-of-the-art prosthetic components" (Frossard et al. Clinical Biomechanics, 89 (2021) 105457. DOI: 10.1016/j.clinbiomech.2021.105457). This article contains the overall and individual loading characteristics applied on transfemoral press-fit osseointegrated implant generated by bone-anchored prostheses fitted with state-of-the-art components during daily activities (i.e., microprocessor-controlled Rheo Knee XC knee, energy-storing-and-returning Pro-Flex XC or LP feet (ÖSSUR, Iceland)). Confounders of the loads are presented. The load profiles are characterized by the loading patterns, loading boundaries and loading local extrema of the forces and moments applied during straight-level walking, ascending and descending ramp and stairs at self-selected comfortable pace. The confounders of the loading information as well as new insights into inter-participants variability of loading patterns, loading boundaries and loading local extrema can inform the design of subsequent cross-sectional and longitudinal studies as well as literature reviews and meta-analyzes. The loading datasets are critical to clinicians and engineers designing finite element models of osseointegrated implants (e.g., medullar and percutaneous parts) and prosthetic components, algorithms capable to recognize the loading patterns applied on a residuum during daily activities, as well as clinical trials assessing the effects of particular prosthetic care interventions. Altogether, these datasets provide promoters of prosthetic care innovations with valuable insights informing the prescription of advanced prosthetic components to the growing population of individuals suffering from lower limb loss choosing bionics solutions. Online repository contains the files: https://data.mendeley.com/datasets/gmsyv97cpc/1.
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