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Record W4400454131 · doi:10.1080/15438627.2024.2377087

The effect of resistance training on patients with knee osteoarthritis: a systematic review and meta-analysis

2024· review· en· W4400454131 on OpenAlexaboutno aff
Tingrui Xu, Borui Zhang, Dongmei Fang

Bibliographic record

VenueResearch in Sports Medicine · 2024
Typereview
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisResistance trainingMedicineOsteoarthritisPhysical therapyPhysical medicine and rehabilitationInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

The objective of this study is to investigate the beneficial effects of resistance training (RT) on individuals suffering from knee osteoarthritis (KOA). In order to gather relevant studies from the beginning of various databases until January 2023, a comprehensive search was conducted on PubMed, Embase, Scopus, Web of Science, and The Cochrane Library. Additionally, manual searches were performed on the reference lists. The association between RT and KOA was analysed using a random-effects model. The results indicated that patients with KOA who underwent RT experienced a significant reduction in the WOMAC (Western Ontario and McMaster Universities Osteoarthritis) Pain index (WMD = −2.441; 95% CI = −3.610 to −1.273; p < 0.01), the WOMAC Stiffness index (WMD = −1.018; 95% CI = −1.744 to −0.293; p < 0.01), the WOMAC Function index (WMD = −7.208; 95% CI = −10.412 to −4.004; p < 0.01), and the VAS (Visual Analogue Scale) index (WMD = −5.721; 95% CI = −9.320 to −2.121; p < 0.01). These improvements were observed when compared to the control group. However, no significant difference was found in the 6-MWT (6-Minute Walk Test) index between the two groups (WMD = 2.659; 95% CI= −16.741 to 22.058; p = 0.788). Consequently, RT has the potential to positively enhance pain, stiffness, and function in patients with KOA, while the 6-MWT index may not exhibit significant improvement.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.490
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0110.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.403
Teacher spread0.311 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2024
Admission routes1
Has abstractyes

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