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Record W2286403847 · doi:10.18869/acadpub.hms.21.2.75

Effect of Muscle Relaxation on Hemodialysis Patients’ Pain

2015· article· en· W2286403847 on OpenAlexaboutno aff
tahere blouchi, Mojtaba Kianmehr, jahanshir tavakolizade, Mahdi Basirimoghadam, Fateme Biabani

Bibliographic record

VenueQuarterly of Horizon of Medical Sciences · 2015
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemodialysisPhysical therapyRelaxation (psychology)McGill Pain QuestionnaireDialysisInclusion and exclusion criteriaIntensity (physics)Psychological interventionRandomized controlled trialSurgeryInternal medicineVisual analogue scaleNursingAlternative medicine

Abstract

fetched live from OpenAlex

Aims: Dialysis patients have experienced some degree of pain, especially foot pain. Some complementary interventions such as muscle relaxation are effective in relieving pain. This study was performed with the aim of assessing the effect of muscle relaxation on hemodialysis patients' pain. Materials & Methods: This randomized controlled clinical trial was conducted on 90 hemodialysis patients of Khatamolanbia and Imam Ali hemodialysis centers of Zahedan during 2013 and 2014. The patients were chosen by purposive sampling based on inclusion criteria and randomly divided into control and experimental groups. Pain intensity was measured by McGill questionnaire before intervention. Then, Benson muscle relaxation was taught to patients' of case group and was performed by them for 15-20 minutes twice a day for a month. The control group received no training. The pain intensity of two groups was compared after one month. The data were analyzed using Chisquare, independent T and Mann-Whitney tests by SPSS 21 software. Findings: Most of the patients were men, married, housekeeper with under diploma education and the mean age of them was 43.015.0 years. There was a significant decrease in pain intensity in the intervention group compared to the control (p=0.03).

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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.301
Teacher spread0.287 · 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 designOther design
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

Citations9
Published2015
Admission routes1
Has abstractyes

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