Evidence map›Paper›PMID 40858207›Full record

ArticleArchives of physical medicine and rehabilitation2026

PROPREDICT Decision Support Tool: Using Evidence to Guide Precision Prosthesis Prescription and Rehabilitation Planning.

Daniel C Norvell, David C Morgenroth, Joseph M Czerniecki, Elizabeth G Halsne, Wayne Biggs, Joseph Webster, Aaron P Turner, Rhonda M Williams, Alison W Henderson

Abstract read
In one paragraph

Article in Archives of physical medicine and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

9 authors.

Daniel C NorvellVA Puget Sound Health Care System, Seattle, WA; VA Center for Limb Loss and MoBility (CLiMB), Seattle, WA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA. Electronic address: Daniel.Norvell@va.gov.
David C MorgenrothVA Puget Sound Health Care System, Seattle, WA; VA Center for Limb Loss and MoBility (CLiMB), Seattle, WA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA.
Joseph M CzernieckiVA Puget Sound Health Care System, Seattle, WA; VA Center for Limb Loss and MoBility (CLiMB), Seattle, WA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA.
Elizabeth G HalsneVA Puget Sound Health Care System, Seattle, WA; VA Center for Limb Loss and MoBility (CLiMB), Seattle, WA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA.
Wayne BiggsVA Puget Sound Health Care System, Seattle, WA.
Joseph WebsterDepartment of Physical Medicine and Rehabilitation, VA Fayetteville Coastal Healthcare System, Fayetteville, NC.
Aaron P TurnerVA Puget Sound Health Care System, Seattle, WA; VA Center for Limb Loss and MoBility (CLiMB), Seattle, WA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA.
Rhonda M WilliamsVA Puget Sound Health Care System, Seattle, WA; VA Center for Limb Loss and MoBility (CLiMB), Seattle, WA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA.
Alison W HendersonVA Puget Sound Health Care System, Seattle, WA; VA Center for Limb Loss and MoBility (CLiMB), Seattle, WA.

Funding

RRD VA I01 RX004528RRD VA I01 RX004825
6 · The paper itself

Abstract

objectiveTo translate the previously validated AMPREDICT PROsthetics prediction model into a web-based, clinical decision support tool (DST) for use at the time of initial prosthetic evaluation to predict 4 levels of prosthetic mobility 12 months after prescription.

designThe previously validated AMPREDICT PROsthetics prediction model provided the predictors with corresponding coefficients to develop an online DST. The content and aesthetics of the DST was created by the authors with iterative input from an expert panel gathered via a user-friendly input page and access to a DST simulation with graphic display of predicted mobility levels. This beta DST underwent formal usability testing using a think aloud approach with a quantitative assessment of usability and qualitative interviews to address user-friendliness, readability, functionality, and potential implementation challenges.

settingThe Veterans Health Administration.

participantsTwelve clinicians (N=12), who regularly participate in prosthetic prescription and represent the intended target users of the PROPREDICT DST, were included.

interventionsNot applicable.

main outcome measuresQualitative themes and the Post-Study System Usability Questionnaire (PSSUQ).

resultsThe PSSUQ overall (and subscale scores) were highly favorable, with a mean overall score of 1.58 from a potential range 1.0-7.0 (lower score greater usability). Participant feedback identified the following potential clinical utility of the DST: (1) assisting in counseling patients on prosthesis selection; (2) setting expectations for future mobility; and (3) sharing DST input with the clinical team to aid in rehabilitation planning.

conclusionsThe PROPREDICT DST (www.prodecide.org) was successfully developed and tested for usability and clinical relevance. Rehabilitation providers are optimistic about its potential.

Indexed as

Artificial LimbsDecision Support Systems, ClinicalDecision Support TechniquesFemaleHumansMaleMiddle AgedUnited StatesUnited States Department of Veterans AffairsAmputationClinical decision support toolsPredictionProstheticsRehabilitation

Identifiers

PMID40858207
PMCPMC12577931

What Socratic holds

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Read underepoch 390

Registered trials

None linked

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.