Evidence map›Paper›PMID 42832544›Full record

ArticlePLOS digital health2026

Toward predictive prosthetic socket modeling: Preliminary linear regression models of transradial limb-socket geometry.

Vishal Pendse, Nader Allam, Calvin C Ngan, Elaine Lorette, Isabelle Morin-Girard, Jan Andrysek

Abstract read
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Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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.

2 · The registry

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3 · Its place in the literature

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Vishal PendseInstitute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.ORCID https://orcid.org/0009-0002-1817-2620
Nader AllamHolland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada.
Calvin C NganHolland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada.
Elaine LoretteHolland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada.
Isabelle Morin-GirardHolland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada.
Jan AndrysekInstitute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Socket fit is essential for successful upper-limb prosthetic devices, yet current design workflows remain labor-intensive and dependent on clinician experience. Several digital and algorithmic approaches have been proposed to partially automate socket design, many of which rely on characterizing conventional design practice. In upper-limb prosthetics, prior research has quantified transradial socket rectification practices, but the direct geometric relationship between the residual limb and final socket remains unclear despite its importance to digital socket design workflows. This study examined whether simple linear regression models could provide a preliminary, interpretable description of limb-socket geometric relationships in transradial socket design. Fourteen participants with transradial limb absence were included, whose digitized limb-socket pairs were aligned using a novel spline-based method. Relationships were quantified using global descriptors (volume, surface area, and proximal-distal length) and cross-sectional descriptors at 25%, 50%, and 75% of limb length (mediolateral length, anterior-posterior length, circumference, and cross-sectional area). For each descriptor, simple linear regressions were fit to predict socket geometry from limb geometry, and model performance was evaluated using leave-one-out cross-validation. Global descriptors demonstrated strong linear relationships, with R² values ranging from 0.94 to 0.97 and predicted R² values from 0.92 to 0.97, indicating that overall socket size is largely predictable from limb size. Cross-sectional descriptors showed more variable performance, with the strongest results at mid-length and weaker relationships toward the proximal and distal ends. Mean absolute percentage error ranged from 2.5% to 13.6% across descriptors, while retention values were generally high, indicating limited overfitting. These findings suggest that simple linear models can capture broad scaling relationships between transradial limbs and sockets, but are less able to explain localized geometry in regions where trimline design, suspension strategy, and distal-end clearance play a greater role.

Identifiers

PMID42832544
PMCPMC13637916

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.