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Record W4408724432 · doi:10.1016/j.jor.2025.03.020

A randomized controlled trial: Acupotomy Arthroscope vs. arthroscopic intervention in knee OA patients' gait and symptoms

2025· article· en· W4408724432 on OpenAlexaboutno aff
Zichao Xiong, Shaodan Cheng, Cheng Ge, Yang Zhang, Shihui Wang, Yunwen Gao, Yinghui Ma

Bibliographic record

VenueJournal of Orthopaedics · 2025
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersScience and Technology Commission of Shanghai Municipality
KeywordsMedicineRandomized controlled trialGaitSurgeryPhysical therapy

Abstract

fetched live from OpenAlex

Background: This study aimed to compare the effects of Acupotomy Arthroscope and Arthroscopic interventions on gait and symptoms in patients with Knee Osteoarthritis (KOA). Methods: In a single-blind, randomized trial, 73 KOA patients were assigned to receive either Acupotomy Arthroscope or Arthroscopic treatment. The primary outcomes measured were pre- and post-intervention gait spatiotemporal and kinematic parameters. Secondary outcomes included the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Knee Society Score (KSS). Results: Both groups exhibited significant improvements in gait parameters and reductions in WOMAC scores, with increases in KSS post-intervention (P < 0.01). The Acupotomy Arthroscope group demonstrated better improvements in gait cycle times and knee flexion angles, although it was less effective in enhancing walking speed. Conclusion: Both interventions effectively enhanced gait biomechanics and reduced joint symptoms. Acupotomy Arthroscope was more effective in improving short-term clinical symptoms and functional capacity, while Arthroscopic treatment was superior for pain and mobility limitations.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0150.001

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 head0.006
GPT teacher head0.272
Teacher spread0.265 · 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 designRandomized 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
Published2025
Admission routes1
Has abstractyes

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