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Record W2883111126 · doi:10.1136/bmjopen-2017-021039

Phantom motor execution as a treatment for phantom limb pain: protocol of an international, double-blind, randomised controlled clinical trial

2018· article· en· W2883111126 on OpenAlexaff
Eva Lendaro, Liselotte Hermansson, Helena Burger, Corry K. van der Sluis, Brian E. McGuire, Monika Pilch, Lina Bunketorp Käll, Katarzyna Kulbacka-Ortiz, Ingrid Rignér, Anita Stockselius, Lena Gudmundson, Cathrine Widehammar, Wendy Hill, Sybille Geers, Max Ortiz-Catalan

Bibliographic record

VenueBMJ Open · 2018
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsUniversity of New Brunswick
FundersVINNOVAStiftelsen Promobilia
KeywordsMedicinePhysical medicine and rehabilitationQuality of life (healthcare)Physical therapyPsychological interventionPhantom limbRandomized controlled trialMoodClinical trialImaging phantomSurgeryPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Phantom limb pain (PLP) is a chronic condition that can greatly diminish quality of life. Control over the phantom limb and exercise of such control have been hypothesised to reverse maladaptive brain changes correlated to PLP. Preliminary investigations have shown that decoding motor volition using myoelectric pattern recognition, while providing real-time feedback via virtual and augmented reality (VR-AR), facilitates phantom motor execution (PME) and reduces PLP. Here we present the study protocol for an international (seven countries), multicentre (nine clinics), double-blind, randomised controlled clinical trial to assess the effectiveness of PME in alleviating PLP. METHODS AND ANALYSIS: Sixty-seven subjects suffering from PLP in upper or lower limbs are randomly assigned to PME or phantom motor imagery (PMI) interventions. Subjects allocated to either treatment receive 15 interventions and are exposed to the same VR-AR environments using the same device. The only difference between interventions is whether phantom movements are actually performed (PME) or just imagined (PMI). Complete evaluations are conducted at baseline and at intervention completion, as well as 1, 3 and 6 months later using an intention-to-treat (ITT) approach. Changes in PLP measured using the Pain Rating Index between the first and last session are the primary measure of efficacy. Secondary outcomes include: frequency, duration, quality of pain, intrusion of pain in activities of daily living and sleep, disability associated to pain, pain self-efficacy, frequency of depressed mood, presence of catastrophising thinking, health-related quality of life and clinically significant change as patient's own impression. Follow-up interviews are conducted up to 6 months after the treatment. ETHICS AND DISSEMINATION: The study is performed in agreement with the Declaration of Helsinki and under approval by the governing ethical committees of each participating clinic. The results will be published according to the Consolidated Standards of Reporting Trials guidelines in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT03112928; Pre-results.

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)0.030
metaresearch head score (Gemma)0.030
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: Protocol
Teacher disagreement score0.043
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.030
Meta-epidemiology (narrow)0.0070.003
Meta-epidemiology (broad)0.0130.004
Bibliometrics0.0030.004
Science and technology studies0.0030.006
Scholarly communication0.0040.003
Open science0.0040.002
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0430.008

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 head0.290
GPT teacher head0.550
Teacher spread0.259 · 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

Citations40
Published2018
Admission routes1
Has abstractyes

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