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Record W2543164452 · doi:10.1186/s40064-016-3535-0

Comparison of a robotic-assisted gait training program with a program of functional gait training for children with cerebral palsy: design and methods of a two group randomized controlled cross-over trial

2016· article· en· W2543164452 on OpenAlexafffund
Alicia Hilderley, Darcy Fehlings, Gloria W. Lee, F. Virginia Wright

Bibliographic record

VenueSpringerPlus · 2016
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsHolland Bloorview Kids Rehabilitation Hospital
FundersBloorview Research InstituteHolland Bloorview Kids Rehabilitation Hospital Foundation
KeywordsCerebral palsyPhysical medicine and rehabilitationGaitGait trainingRandomized controlled trialPhysical therapyTraining (meteorology)MedicineRehabilitationComputer scienceSurgery

Abstract

fetched live from OpenAlex

Enhancement of functional ambulation is a key goal of rehabilitation for children with cerebral palsy (CP) who experience gross motor impairment. Physiotherapy (PT) approaches often involve overground and treadmill-based gait training to promote motor learning, typically as free walking or with body-weight support. Robotic-assisted gait training (RAGT), using a device such as the Lokomat®Pro, may permit longer training duration, faster and more variable gait speeds, and support walking pattern guidance more than overground/treadmill training to further capitalize on motor learning principles. Single group pre-/post-test studies have demonstrated an association between RAGT and moderate to large improvements in gross motor skills, gait velocity and endurance. A single published randomized controlled trial (RCT) comparing RAGT to a PT-only intervention showed no difference in gait kinematics. However, gross motor function and walking endurance were not evaluated and conclusions were limited by a large PT group drop-out rate. In this two-group cross-over RCT, children are randomly allocated to the RAGT or PT arm (each with twice weekly sessions for eight weeks), with cross-over to the other intervention arm following a six-week break. Both interventions are grounded in motor learning principles with incorporation of individualized mobility-based goals. Sessions are fully operationalized through manualized, menu-based protocols and post-session documentation to enhance internal and external validity. Assessments occur pre/post each intervention arm (four time points total) by an independent assessor. The co-primary outcomes are gross motor functional ability (Gross Motor Function Measure (GMFM-66) and 6-minute walk test), with secondary outcome measures assessing: (a) individualized goals; (b) gait variables and daily walking amounts; and (c) functional abilities, participation and quality of life. Investigators and statisticians are blinded to study group allocation in the analyses, and assessors are blinded to treatment group. The primary analysis will be the pre- to post-test differences (change scores) of the GMFM-66 and 6MWT between RAGT and PT groups. This study is the first RCT comparing RAGT to an active gait-related PT intervention in paediatric CP that addresses gait-related gross motor, participation and individualized outcomes, and as such, is expected to provide comprehensive information as to the potential role of RAGT in clinical practice. Trial registration ClinicalTrials.gov NCT02196298

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)0.010
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: Protocol · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0090.005
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0100.002

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 head0.068
GPT teacher head0.389
Teacher spread0.320 · 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
GenreProtocol

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

Citations33
Published2016
Admission routes2
Has abstractyes

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