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Record W4383955019 · doi:10.4283/jmag.2023.28.2.208

Effects of Repetitive Transcranial Magnetic Stimulation on Neuropathic Pain and Walking Ability in Patients with Incomplete Spinal Cord Injury

2023· article· en· W4383955019 on OpenAlexaboutno aff
Hyun-Gyu Cha

Bibliographic record

VenueJournal of Magnetics · 2023
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTranscranial magnetic stimulationNeuropathic painSpinal cord injuryPhysical therapySpinal cordPhysical medicine and rehabilitationAnesthesiaVisual analogue scaleMcGill Pain QuestionnaireGaitSignificant differenceStimulationInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the effects of rTMS on neuropathic pain and walking ability in patients with iSCI. 10 subjects were assigned to each of the experimental group (10 Hz rTMS) and the control group (5 Hz rTMS group). The rTMS intervention was administered 5 times a week for 20 minutes each time for 6 weeks. All measurements were performed before rTMS intervention and 6 weeks after rTMS intervention. In this study, VAS (Visual Analog Scale) and SF-MPQ (Short Form - McGill Pain Questionnaire) were applied to evaluate the pain of patients with spinal cord injuries. Gait endurance was evaluated by the 6-minute walking test (6MWT), and walking speed was evaluated by the 10-m walking test (10MWT). In the comparison between each group, the experimental group showed significant differences in the post-intervention SFMPQ, 6-minute walking test, and 10-meter walking test (p 0.05), and the control group showed a significant difference in the 10-minute walking test (p 0.05). In the comparison between the two groups, there was no significant difference in all variables after intervention (p 0.05). High-frequency rTMS can help reduce neuropathic pain in clinical practice and improve walking ability in patients with incomplete spinal cord injury.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2023
Admission routes1
Has abstractyes

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