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Record W7018636119

Effect of Neuromuscular Exercise Training Combined with Semiconductor Laser Therapy on Patients with Knee Osteoarthritis

2025· article· en· W7018636119 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisVisual analogue scaleBerg Balance ScaleRehabilitationBalance (ability)Squatting positionElectromyographyHamstring
DOInot available

Abstract

fetched live from OpenAlex

ObjectiveTo observe the effect of neuromuscular exercise (NEMEX训练) training combined with semiconductor laser therapy on patients with knee osteoarthritis (KOA).MethodsA total of sixty patients with knee osteoarthritis were treated in the Rehabilitation Medicine Department of the First Affiliated Hospital of Bengbu Medical University from August 2023 to March 2024 were selected and randomly divided into a control group and an observation group using a random number table method, with 30 patients in each group. The control group received routine rehabilitation treatment (joint loosening, intermediate frequency electrical stimulation) and semiconductor laser treatment, with a laser wavelength of 808 nm and an irradiation power of 400-800 mW, continuous irradiation, each treatment lasting 20 minutes/time, once a day, 5 times a week, for a total of 4 weeks. The observation group received NEMEX训练 on the basis of the control group. Warm up exercise for 10 minutes per session; NEMEX训练 (stability training, posture orientation training, lower limb muscle strength training, functional exercise) 40 minutes per session; Relaxation training (gait adjustment and stretching) for 10 minutes per session, 1 session per day, 5 sessions per week, for a total of 4 weeks. Visual Analog Scale (VAS) was used to assess the degree of pain before and after treatment; Evaluate knee joint function using the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and Lysholm Knee Score; Use Berg Balance Scale (BBS) to evaluate balance function; Using a surface electromyography system to record the root mean square (RMS) of surface electromyographic signals (sEMG) of the quadriceps and hamstring muscles during the squatting process of the affected knee joint, and calculate the coordinated contraction rate (CR); Compare the adverse events during the treatment process of two groups.Results(1) VAS, WOMAC, Lysholm, and BBS scores:Compared with before treatment, VAS and WOMAC scores in both groups decreased significantly after treatment (P<0.05), while Lysholm and BBS scores increased significantly, and the differences were statistically significant (P<0.05). Compared with the control group, the observation group showed a significant decrease in VAS and WOMAC scores after treatment (P<0.05), while Lysholm and BBS scores were significantly higher, and the differences were statistically significant (P<0.05). (2) CR:Compared with before treatment, the observation group showed a significant decrease in CR after treatment, and the differences were statistically significant (P<0.05), while the control group showed no statistically significant difference in CR after treatment (P>0.05). Compared with the control group, the observation group showed a significant decrease in CR after treatment, and the differences were statistically significant (P<0.05). (3) Safety:During the treatment process, there were no adverse events in both groups, and the patient compliance was good with high safety.ConclusionNEMEX training combined with semiconductor laser therapy can effectively improve pain, knee joint function, balance function, and muscle coordination and control around the knee joint in patients with knee osteoarthritis, and is worthy of clinical promotion and application.

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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.000

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 head0.078
GPT teacher head0.475
Teacher spread0.397 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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