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Record W3191980927

동적 균형 훈련이 만성 슬관절 관절염 환자의 통증, 신체 기능과 균형 능력에 미치는 영향

2018· article· ko· W3191980927 on OpenAlexaboutno aff
방대혁, 봉순녕

Bibliographic record

VenuePNF and Movement · 2018
Typearticle
Languageko
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
Fundersnot available
KeywordsBalance (ability)OsteoarthritisPhysical therapyWOMACDynamic balanceBalance trainingMedicinePhysical medicine and rehabilitationAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Purpose: The aim of this study was to explore the effects of dynamic balance training on pain, physical function, and dynamic balance in individuals with knee osteoarthritis. Methods: Fourteen patients with knee osteoarthritis participated in this study. The patients were randomly assigned to two groups: an experimental group (n=7) or a control group (n=7). All the patients took part in a lower extremity strength program for 30 min. In addition, the experimental group participated in a 30-min dynamic balance program. Both groups performed the program five times a week for 3 weeks. Outcomes, including the numeric rating scale (NRS), Western Ontario and MacMaster Universities Arthritis Index (WOMAC), and Community Balance and Mobility Scale (CBM Z = -2.82) and CBM Z = -2.20) scores after the intervention as compared with those of the control group. Conclusion: The results revealed that dynamic balance training improved physical function, as well as balance ability, in patients with knee osteoarthritis as compared with that of a control group with no balance training.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0030.000

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 head0.064
GPT teacher head0.406
Teacher spread0.342 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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