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Record W2362922642

The Effect of Electrical Muscle Stimulation Therapy on Chronic Knee Pain for Aged

2005· article· en· W2362922642 on OpenAlexaboutno aff
Sohyune Sok, Kwuy‐Bun Kim

Bibliographic record

Venuejournal of east-west nursing research · 2005
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical therapyStimulationMcGill Pain QuestionnaireChronic painKnee painRandomized controlled trialFunctional electrical stimulationPhysical medicine and rehabilitationAnesthesiaVisual analogue scaleOsteoarthritisInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Purpose: This study is to examine the effect of electrical muscle stimulation therapy on chronic knee pain for aged. Method: Design was randomized one-group pretest-posttest design. Samples were total 15 elderly on 60 years old and above with chronic knee pain. Measures were S-F McGill Pain Questionnaire and Arthritis Impact Measurement Scale for knee pain. Electrical muscle stimulation therapy, experimental treatment was applied on chronic knee pain for 4 weeks, 3 times/week, 15 min/time. Data were collected from half March 2005 to May 2005. Data were analyzed using SPSS PC+ 12 version. Descriptive statistics was used for analysis of general characteristics in sample, and paired t-test was used to analysis the effect of electrical muscle stimulation therapy. Results: After receiving the electrical muscle stimulation therapy chronic knee pain was significantly decreased (t=-29.163, P=.000 in S-F MPQ; t=-37.005, P=.000 in AIMS). Conclusion: Electrical muscle stimulation therapy can be a better effective primary nursing intervention on chronic knee pain for aged in community.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0030.000

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 head0.101
GPT teacher head0.531
Teacher spread0.430 · 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 designNon-randomized trial
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

Citations0
Published2005
Admission routes1
Has abstractyes

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