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Record W2467096835 · doi:10.2196/resprot.5645

Optimizing Rehabilitation for Phantom Limb Pain Using Mirror Therapy and Transcranial Direct Current Stimulation: A Randomized, Double–Blind Clinical Trial Study Protocol

2016· article· en· W2467096835 on OpenAlexvenueno aff
Camila Bonin Pinto, Faddi G. Saleh Velez, Nadia Bolognini, David Crandell, Lotfi B. Merabet, Felipe Fregni

Bibliographic record

VenueJMIR Research Protocols · 2016
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of Health
KeywordsTranscranial direct-current stimulationPhysical medicine and rehabilitationPhantom limbNeuroplasticityPhantom limb painBrain stimulationMedicineRandomized controlled trialRehabilitationTranscranial magnetic stimulationPhysical therapyNeuromodulationStimulationChronic painPsychologyNeuroscienceAmputationSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the multiple available pharmacological and behavioral therapies for the management of chronic phantom limb pain (PLP) in lower limb amputees, treatment for this condition is still a major challenge and the results are mixed. Given that PLP is associated with maladaptive brain plasticity, interventions that promote cortical reorganization such as non-invasive brain stimulation and behavioral methods including transcranial direct current stimulation (tDCS) and mirror therapy (MT), respectively, may prove to be beneficial to control pain in PLP. Due to its complementary effects, a combination of tDCS and MT may result in synergistic effects in PLP. OBJECTIVE: The objective of this study is to evaluate the efficacy of tDCS and MT as a rehabilitative tool for the management of PLP in unilateral lower limb amputees. METHODS: A prospective, randomized, placebo-controlled, double-blind, factorial, superiority clinical trial will be carried out. Participants will be eligible if they meet the following inclusion criteria: lower limb unilateral traumatic amputees that present PLP for at least 3 months after the amputated limb has completely healed. Participants (N=132) will be randomly allocated to the following groups: (1) active tDCS and active MT, (2) sham tDCS and active MT, (3) active tDCS and sham MT, and (4) sham tDCS and sham MT. tDCS will be applied with the anodal electrode placed over the primary motor cortex (M1) contralateral to the amputation side and the cathode over the contralateral supraorbital area. Stimulation will be applied at the same time of the MT protocol with the parameters 2 mA for 20 minutes. Pain outcome assessments will be performed at baseline, before and after each intervention session, at the end of MT, and in 2 follow-up visits. In order to assess cortical reorganization and correlate with clinical outcomes, participants will undergo functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) before and after the intervention. RESULTS: This clinical trial received institutional review board (IRB) approval in July of 2015 and enrollment started in December of 2015. To date 2 participants have been enrolled. The estimate enrollment rate is about 30 to 35 patients per year; thus we expect to complete enrollment in 4 years. CONCLUSIONS: This factorial design will provide relevant data to evaluate whether tDCS combined with MT is more effective than each therapy alone, as well as with no intervention (sham/sham) in patients with chronic PLP after unilateral lower limb amputation. In addition, this randomized clinical trial will help to investigate the neurophysiological mechanisms underlying the disease, which could potentially provide relevant findings for further management of this chronic condition and also help to optimize the use of this novel intervention. TRIAL REGISTRATION: Clinicaltrials.gov NCT02487966; https://clinicaltrials.gov/ct2/show/NCT02487966 (Archived by WebCite at http://www.webcitation.org/6i3GrKMyf).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Protocol
Teacher disagreement score0.099
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.594
GPT teacher head0.648
Teacher spread0.053 · 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 teacher head, 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

Citations40
Published2016
Admission routes1
Has abstractyes

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