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Record W2888646228 · doi:10.5606/tftrd.2018.1384

Osteopathic manipulative treatment improves function and relieves pain in knee osteoarthritis: A single-blind, randomized-controlled trial

2018· article· en· W2888646228 on OpenAlexaboutno aff
Turgay Altınbilek

Bibliographic record

VenueTurkish Journal of Physical Medicine and Rehabilitation · 2018
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisWOMACPhysical therapyRandomized controlled trialVisual analogue scaleRange of motionKnee painSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: In this study, we aimed to compare the efficacy of osteopathic manipulative treatment (OMT) to exercise treatment in the knee osteoarthritis (OA). PATIENTS AND METHODS: A total of 100 patients (9 males, 76 females; mean age 54.8±8.5 years; range, 40 to 70 years) with Stage II-III bilateral knee OA enrolled to the study and randomized into two groups between January 2015 and June 2015. Group 1 performed exercise and received OMT and Group 2 performed exercise alone. We assessed the clinical parameters with Western Ontario MacMaster Questionnaire (WOMAC) pain score, WOMAC joint stiffness score, WOMAC physical function score, Visual Analog Scale (VAS) and 50-m walking time. All patients were assessed at the beginning of the study, just after the treatment, and four weeks after the treatment. RESULTS: There was no significant difference between groups in terms of physical examination and clinical assessment parameters before treatment. Functional improvement (p<0.05) and pain relief (p<0.05) were significantly higher in the exercise + OMT group. CONCLUSION: Based on our study results, OMT is a particular treatment used by osteopathic physicians to complement conventional treatment of OA of the knee. In addition to the conservative treatment, OMT can be used.

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)0.002
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.016
GPT teacher head0.278
Teacher spread0.262 · 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

Citations32
Published2018
Admission routes1
Has abstractyes

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