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Record W2311903408 · doi:10.22059/jsmed.2013.32161

The Effects of a Pilates Training Program on the Function and Pain of Patients with Disc Herniation with Lumbosciatalgia

2013· article· en· W2311903408 on OpenAlexaboutno aff
Masod Golpaygani, Solmaz Mahdavi, Leili Moradi

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2013
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical therapyMedicineRange of motionLumbar disc herniationLow back painPhysical medicine and rehabilitationLumbarMcGill Pain QuestionnaireSurgeryVisual analogue scale

Abstract

fetched live from OpenAlex

The aim of this study was to determine the effect of a pilates training program on the pain and function of patients with disc herniation with lumbosciatalgia and to compare this exercise procedure with the general protocol. For this purpose, 34 patients with disc herniation with lumbosciatalgia participated as the sample in this study and were divided into experimental (n=16) and control (n=18) groups randomly. Experimental group performed pilates exercise for 4 weeks, three sessions per week and 45 – 60 min. per session. At the same time, control group performed the general protocol for 4 weeks. The range of motion of lumbar and hip was measured with Schober and passive straight leg raising (SLR) test respectively. Pain and disability was measured with McGill and Oswestry questionnaires respectively. Independent and dependent t tests were used to analyze the data. The results showed that pilates exercise increased SLR angle and range of motion of lumbar and decreased patients' pain and disability (P≤0.05). It seems that pilates exercise improved pain and function of patients with disc herniation with lumbosciatalgia.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.059
GPT teacher head0.437
Teacher spread0.379 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized 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

Citations1
Published2013
Admission routes1
Has abstractyes

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