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Record W2763861181 · doi:10.1161/str.48.suppl_1.tmp38

Abstract TMP38: Virtual Reality in Stroke Rehabilitation: Identifying Responders in Evrest Multicentre Trial

2017· article· en· W2763861181 on OpenAlexaff
Gustavo Saposnik, Steve Cramer, Leonardo G. Cohen, Ashley Cohen, Andreas Laupacis, Mark Bayley

Bibliographic record

VenueStroke · 2017
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsToronto Rehabilitation InstituteSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMinimal clinically important differencePhysical therapyStroke (engine)RehabilitationRandomized controlled trialIntervention (counseling)Physical medicine and rehabilitationSurgeryNursing

Abstract

fetched live from OpenAlex

Introduction: Despite the modest benefits of non-immersive virtual reality (VR) in small, single center studies, our largest trial (EVREST Muticentre) showed no significant difference in motor recovery when VR was compared to an active control. More crucial is to determine the presence of a treatment effect by evaluating respondents. Methods: Adults <3 months of stroke with a Chedoke-McMaster >3 were randomized to receive VR using the Nintendo Wii™ gaming system (VRWii) vs. recreational activities (playing cards, ‘Jenga’, domino) (RA). All participants received usual care consisting of conventional rehabilitation at each center. Participants received an intensive program of 10 sessions of either VR or RA, 60 minutes each, over a 2-week period. The primary outcome was a difference in motor performance between groups using the Wolf Motor Function test (WMFT) at the end of the intervention. We defined respondents based on the accepted minimally clinically important difference (MCID) of ≥20% improvement from the baseline WMFT. 1 Secondary outcomes included a MCID of 30% in the Stroke impact Scale (hand) and in the perception of improvement. 2 Results: Between May 2012 and Oct, 2015, 141 patients received either VRWii (n=71) or RA (n=70). Mean age was 62±12 years. Overall, 63 (53%) participants achieved the MCID (47% % in the VRWii vs 58% RA; p=0.32) at the end of the intervention and 81% 4-weeks post intervention (74 % in the VRWii vs 87% RA; p= 0.21). The total duration of each intervention between respondents and non-respondents was similar (589±57 vs. 579±31 min; p=0.47). Multivariable analysis revealed no difference in the response to VRWii compared to RA (OR 0.63; 95%CI 0.30-1.33). Other outcomes are summarized in the Table. Conclusions: The responder analysis in EVREST Multicenter showed no significant difference between groups (VRWii vs RA) for the primary and secondary outcomes. Our results are in agreement with prior analyses that compared mean change across groups.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.048
GPT teacher head0.365
Teacher spread0.317 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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