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Record W2910225507 · doi:10.4172/2167-7921.1000277

Sinergic Effect of Therapeutic Ultrasound and Low-Level Laser Therapy in the Treatment of Hands and Knees Ostheoarthritis

2018· article· en· W2910225507 on OpenAlexaboutno aff
De Souza Simao ML, Fernandes Ac, Casarino RL, Zanchin AL, Heloísa Ciol, Antônio Eduardo de Aquino, Bagnato VS

Bibliographic record

VenueJournal of Arthritis · 2018
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLow level laser therapyUltrasoundLaser therapyTherapeutic ultrasoundPhysical therapyLaserRadiology

Abstract

fetched live from OpenAlex

Osteoarthritis is a degenerative joint disease that affects predominantly hands and knees of the elderly population, being characterized by chronic pain and limitation of joint movements. Therapeutic approaches to ease the pain, as low-intensity pulsed ultrasound and photobiomodulation (low-level laser therapy) have been broadly used as a complement to drug treatment of osteoarthritis. The aim of this study was to evaluate the synergic effect of LIPUS associated to LLLT on osteoarthritis of hands and knees. For this, 69 patients, being 48 affected by knee osteoarthritis and 21 affected by hand osteoarthritis were selected for the study. Patients were evaluated by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) for knee osteoarthritis and by Australian Canadian Osteoarthritis Hand (AUSCAN), for hands osteoarthritis. All patients filled out the Visual Analogue Scale (VAS) pain questionnaire, which showed statistical improvement of hands (p<0.001) and knees (p<0.001) when comparing before and after treatment. Functional evaluation by AUSCAN showed improvement of hands functionality (p<0.002). Results showed that the synergic therapy of LIPUS and LLLT were efficient in the treatment of hands and knees osteoarthritis, providing a new approach of a non-pharmacological and non-invasive treatment that contributes to better quality of life for the patients with this chronic and degenerative pathology.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.793
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.292
Teacher spread0.277 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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