Trial reportTrials2022
Statistical analysis plan for an international, double-blind, randomized controlled clinical trial on the use of phantom motor execution as a treatment for phantom limb pain.
Trial report in Trials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03112928 (Phantom Motor Execution Via Myoelectric Pattern Recognition, Virtual and Augmented Reality, and Serious Gaming as a Treatment of Phantom Limb Pain), which is not on this map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Phantom Motor Execution Via Myoelectric Pattern Recognition, Virtual and Augmented Reality, and Serious Gaming as a Treatment of Phantom Limb Pain
Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
- Trial
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPhantom limb pain (PLP) is a detrimental condition that can greatly diminish the quality of life. Purposeful control over the phantom limb activates the affected neural circuitry and leads to dissolution of the pathological relationship linking sensorimotor and pain processing (which gives rise to PLP). An international, double-blind, randomized controlled clinical trial (RCT) on the use of phantom motor execution (PME) as a treatment for PLP is currently undertaken, where PME is compared to an active placebo treatment, namely phantom motor imagery (PMI). METHODS AND
designSixty-seven subjects suffering from PLP in upper or lower limbs are randomly assigned in 2:1 ratio to PME or PMI interventions respectively. Subjects allocated to either treatment receive 15 interventions where they are exposed to the same VR-AR environments using the same device. The only difference between interventions is whether phantom movements are performed (PME) or imagined (PMI).
resultsThe primary outcome of the study is to examine whether 15 sessions of PME can induce a greater PLP relief, compared to PMI. The secondary objectives are to examine whether 15 sessions of PME provide a greater improvement in different aspects related to PLP compared to PMI, such as pain duration, pain intensity as measured by other metrics, and the patient's own impression about the effect of treatment. Long-term retention of treatment benefits will be assessed as change in all the variables (both primary and secondary) between baseline and follow-up timepoints (at 1, 3, and 6 months post-treatment).
conclusionThis manuscript serves as the formal statistical analysis plan (version 1.0) for the international, double-blind, randomized controlled clinical trial on the use of PME as a treatment for PLP. The statistical analysis plan was completed on 3 August 2021.
trial registrationClinicalTrials.gov NCT03112928 . Registered on April 13, 2017 SAP version: version: 1.0, date: 2021/08/03 Protocol version: This document has been written based on information contained in the study protocol published in Lendaro et al. (BMJ Open 8:e021039, 2018), in July 2018. SAP revisions: Not applicable.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.