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Record W2110732164 · doi:10.1016/j.jmpt.2010.12.001

Augmented Soft Tissue Mobilization vs Natural History in the Treatment of Lateral Epicondylitis: A Pilot Study

2011· article· en· W2110732164 on OpenAlexaff
Marc‐André Blanchette, Martin Normand

Bibliographic record

VenueJournal of Manipulative and Physiological Therapeutics · 2011
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsUniversité de MontréalUniversité du Québec à Trois-RivièresCanadian Chiropractic Association
Fundersnot available
KeywordsEpicondylitisMedicineVisual analogue scaleTennis elbowRandomized controlled trialGrip strengthPhysical therapyElbowClinical trialPhysical medicine and rehabilitationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to evaluate the effect of augmented soft tissue mobilization (ASTM) on the treatment of lateral epicondylitis. METHODS: This randomized clinical study assessed 27 subjects (12 men and 15 women) with lateral epicondylitis and were divided randomly into 2 groups. The experimental group (n = 15) received ASTM twice a week for 5 weeks. The subjects of the control group (n = 12) received advice on the natural evolution of lateral epicondylitis, computer ergonomics, and stretching exercises. Patient-rated outcome was assessed at baseline and after 6 weeks and 3 months using a visual analog scale and the Patient-Rated Tennis Elbow Evaluation. The function was assessed using the pain-free grip strength at baseline and after 6 weeks. RESULTS: Both groups showed improvements in pain-free grip strength, visual analog scale, and Patient-Rated Tennis Elbow Evaluation. Sample size for larger future randomized clinical trial was 116 participants. CONCLUSION: A larger study investigating the same hypothesis is warranted to detect difference in the effects of these treatments strategies. The study design is feasible, and minor improvements will help to minimize the potential bias.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.242

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 head0.386
GPT teacher head0.367
Teacher spread0.019 · 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 designObservational
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

Citations68
Published2011
Admission routes1
Has abstractyes

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