Trial reportPloS one2020
Five-day rehabilitation of patients undergoing total knee arthroplasty using an end-effector gait robot as a neuromodulation blending tool for deafferentation, weight offloading and stereotyped movement: Interim analysis.
Trial report in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Artificial intelligence in the biomechanics of knee osteoarthritis: biomechanical phenotyping, risk stratification, diagnosis, treatment and clinical translation-a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Research trends and frontiers in rehabilitation after total knee arthroplasty: based on bibliometric and visualization analysis.Journal of orthopaedic surgery and research · 2024Article
- Comparing the Lower-Limb Muscle Activation Patterns of Simulated Walking Using an End-Effector-Type Robot with Real Level and Stair Walking in Children with Spastic Bilateral Cerebral Palsy.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deafferentation and weight offloading can increase brain and spinal motor neuron excitability, respectively. End-effector gait robots (EEGRs) can blend these effects with stereotyped movement-induced neuroplasticity. The authors aimed to evaluate the usefulness of EEGRs as a postoperative neuro-muscular rehabilitation tool. This prospective randomized controlled trial included patients who had undergone unilateral total knee arthroplasty (TKA). Patients were randomly allocated into two groups: one using a 200-step rehabilitation program in an EEGR or the other using a walker on a floor (WF) three times a day for five weekdays. The two groups were compared by electrophysiological and biomechanical methods. Since there were no more enrollments due to funding issues, interim analysis was performed. Twelve patients were assigned to the EEGR group and eight patients were assigned to the WF group. Although the muscle volume of the quadriceps and hamstring did not differ between the two groups, the normalized peak torque of the operated knee flexors (11.28 ± 16.04 Nm/kg) was improved in the EEGR group compared to that of the operated knee flexors in the WF group (4.25 ± 14.26 Nm/kg) (p = 0.04). The normalized compound motor action potentials of the vastus medialis (VM) and biceps femoris (BF) were improved in the EEGR group (p < 0.05). However, the normalized real-time peak amplitude and total, mean area under the curve of VM were decreased during rehabilitation in the EEGR group (p < 0.05). No significant differences were found between operated and non-operated knees in the EEGR group. Five-day EEGR-assisted rehabilitation induced strengthening in the knee flexors and the muscular reactivation of the BF and VM after TKA, while reducing the real-time use of the VM. This observation may suggest the feasibility of this technique: EEGR modulated the neuronal system of the patients rather than training their muscles. However, because the study was underpowered, all of the findings should be interpreted with the utmost caution.
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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.