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Record W2969537950 · doi:10.1111/ijn.12772

Application of heat and a home exercise program for pain and function levels in patients with knee osteoarthritis: A randomized controlled trial

2019· article· en· W2969537950 on OpenAlexaboutno aff
Songül Karadağ, Sultan Taşçı, Nurhan Doğan, Hüseyin Demir, Züleyha Kılıç

Bibliographic record

VenueInternational Journal of Nursing Practice · 2019
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisPhysical therapyMedicineVisual analogue scaleRandomized controlled trialKnee painPhysical medicine and rehabilitationSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

AIM: This study aimed to determine the effect of application of superficial local heat and a home exercise program on pain and function levels to patients with bilateral knee osteoarthritis. METHODS: This study was conducted in Turkey between January 2014 and February 2015. The sample group of the study consisted of 62 patients with osteoarthritis; 15 assigned to heat application, 15 to exercise, 15 to exercise after heat application, and 17 for the control group. While the patients in the control group received routine treatment only, the patients in the intervention group were treated with heat application, exercise, or exercise after heat application, suggested for 5 days a week for 4 weeks in addition to routine treatment. RESULTS: In this study, all of the intervention groups had decreases in Visual Analogue Scale Pain and Western Ontario and McMaster Universities Osteoarthritis Index pain, stiffness, and function scores when compared with the control group. It was found that this decrease in Visual Analogue Scale Pain and Western Ontario and McMaster Universities Osteoarthritis Index scores was mostly in the exercise group, but this condition was not statistically significant. CONCLUSIONS: As a result, it is recommended that nurses train patients with osteoarthritis on heat application and home exercises and encourage them to apply these practices.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.297
Teacher spread0.288 · 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 designRandomized 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

Citations25
Published2019
Admission routes1
Has abstractyes

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