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Record W4313473263 · doi:10.3390/healthcare11010122

Effect of Combined Exercise Program on Lower Extremity Alignment and Knee Pain in Patients with Genu Varum

2022· article· en· W4313473263 on OpenAlexaboutno aff
Hyung-Hoon Moon, Yong-Gon Seo, Won-Moon Kim, Jae-Ho Yu, Hae-Lim Lee, Yun-Jin Park

Bibliographic record

VenueHealthcare · 2022
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
Fundersnot available
KeywordsGenu varumGenu ValgumMedicineKnee painPhysical therapyAnkleOsteoarthritisPhysical medicine and rehabilitationOrthodonticsSurgery

Abstract

fetched live from OpenAlex

This study aimed to assess the effect of a combined exercise program on lower-extremity alignment and knee pain in patients with genu varum. Forty-seven middle-aged women with knee pain and genu varum were randomly divided into the exercise (EG, n = 24) and control (CG, n = 23) groups. The exercise group underwent a combined exercise program lasting 60 min in one session, three times a week for 12 weeks. Knee-to-knee length (KTKL), hip-knee-ankle angle (HKAA), hip inclination angle (HIA), and medial proximal tibial angle (MPTA) were assessed to evaluate lower-extremity alignment. To evaluate knee pain, the short form-McGill Pain Questionnaire (SF-MPQ) were used. There was a significant difference between the groups, and a decrease of 16% in KTKL (from 6.48 ± 1.26 cm to 5.47 ± 1.21 cm) was shown in EG. Other variables, including HKAA, HIA, and MPTA on the right side, showed significant differences between pre- and post-intervention in EG (p < 0.01, p < 0.01, and p < 0.01, respectively). SF-MPQ score improved with 45% from 18.75 ± 1.64 to 10.33 ± 2.47 after exercise intervention in EG. These results suggest that the combined exercise program, including strength and neuromuscular exercises, is an effective intervention for improving lower-extremity alignment and knee pain in middle-aged women with genu varum.

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 score0.478
Threshold uncertainty score0.303

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 head0.005
GPT teacher head0.263
Teacher spread0.258 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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