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Record W3168650932

맥켄지 & 윌리엄운동과 골반 저 근육 운동이 만성요통 환자에 미치는 영향

2012· article· ko· W3168650932 on OpenAlexaboutno aff
허진강, 정민근, 우영근, 이주상, 김성신, 김희수

Bibliographic record

Venue한국스포츠학회지 · 2012
Typearticle
Languageko
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical therapyMuscle strengthMcGill Pain QuestionnairePelvic Floor MuscleLow back painOswestry Disability IndexIntensity (physics)Physical medicine and rehabilitationPelvic floorVisual analogue scaleSurgery
DOInot available

Abstract

fetched live from OpenAlex

Objective: This study examined the effects of McKenzie & Williams exercise and pelvic floor muscle exercise after applying conservative physical therapy to chronic low back pain patients. The patients were evaluated for their pain intensity, muscle strength, and muscle endurance. Method: Sixty-eight outpatients were divided into a McKenzie & Williams exercise group and a pelvic floor muscle exercise group. Each group performed its own exercise 30 minutes per day, three times per week, for 8 weeks. Pain intensity was measured by the McGill Pain Questionnaire and the Oswestry Disability Index. Muscle strength and endurance were also measured. Measurements were made at baseline, and again at four weeks and eight weeks after the intervention. Results: The McGill Pain Questionnaire scores, Oswestry Disability Index scores, muscle strength, and muscle endurance for each group showed significant changes at baseline, and at four and eight weeks after the intervention (p<.05). Significant differences were also evident between the two groups for these four measurements (p<.05). Conclusion: Pelvic floor muscle exercise is more effective than McKenzie & Williams exercise for reducing pain intensity and increasing muscle strength and endurance in chronic low back pain patients.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.019

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 head0.156
GPT teacher head0.474
Teacher spread0.318 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2012
Admission routes1
Has abstractyes

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