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Record W4282825966 · doi:10.2196/38704

Digital Graphic Follow-up Tool (Rehabkompassen) for Identifying Rehabilitation Needs Among People After Stroke: Randomized Clinical Feasibility Study

2022· article· en· W4282825966 on OpenAlexvenueno aff
Xiaolei Hu, Karolina Jonzén, Olof Lindahl, Marcus Karlsson, Fredrik Norström, Erik Lundström, Katharina S. Sunnerhagen

Bibliographic record

VenueJMIR Human Factors · 2022
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRehabilitationRandomized controlled trialStroke (engine)Physical therapyIntervention (counseling)Outpatient clinicClinical trialPhysical medicine and rehabilitationSurgeryNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Stroke is a leading cause of disability among adults, with heavy social and economic burden worldwide. A cost-effective solution is urgently needed to facilitate the identification of individual rehabilitation needs and thereby provide tailored rehabilitations to reduce disability among people who have had a stroke. A novel digital graphic follow-up tool Rehabkompassen has recently been developed to facilitate capturing the multidimensional rehabilitation needs of people who have had a stroke. OBJECTIVE: The aim of this study was to evaluate the feasibility and acceptability of conducting a definitive trial to evaluate Rehabkompassen as a digital follow-up tool among people who have had a stroke in outpatient clinical settings. METHODS: This pilot study of Rehabkompassen was a parallel, open-label, 2-arm prospective, proof-of-concept randomized controlled trial (RCT) with an allocation ratio of 1:1 in a single outpatient clinic. Patients who have had a stroke within the 3 previous months, aged ≥18 years, and living in the community were included. The trial compared usual outpatient visits with Rehabkompassen (intervention group) and without Rehabkompassen (control group) at the 3-month follow-up as well as usual outpatient visit with Rehabkompassen at the 12-month follow-up. Information on the recruitment rate, delivery, and uptake of Rehabkompassen; assessment and outcome measures completion rates; the frequency of withdrawals; the loss of follow-up; and satisfaction scores were obtained. The key outcomes were evaluated in both groups. RESULTS: In total, 28 patients (14 control, 14 Rehabkompassen) participated in this study, with 100 patients screened. The overall recruitment rate was 28% (28/100). Retention in the trial was 86% (24/28) at the 12-month follow-up. All participants used the tool as planned during their follow-ups, which provided a 100% (24/24) task completion rate of using Rehabkompassen and suggested excellent feasibility. Both patient- and physician-participants reported satisfaction with the instrument (19/24, 79% and 2/2, 100%, respectively). In all, 2 (N=2, 100%) physicians and 18 (N=24, 75%) patients were willing to use the tool in the future. Furthermore, modified Rankin Scale as the primary outcome and various stroke impacts as secondary outcomes were both successfully collected and compared in this study. CONCLUSIONS: This study demonstrated the high feasibility and adherence of the study protocol as well as the high acceptability of Rehabkompassen among patients who have had a stroke and physicians in an outpatient setting in comparison to the predefined criterion. The information collected in this feasibility study combined with the amendments of the study protocol may improve the future definitive RCT. The results of this trial support the feasibility and acceptability of conducting a large definitive RCT. TRIAL REGISTRATION: ClinicalTrials.gov NCT04915027; https://clinicaltrials.gov/ct2/show/NCT04915027.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0140.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.363
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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