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Record W2122498223 · doi:10.1177/0300060513488509

The effect of mud therapy on pain relief in patients with knee osteoarthritis: A meta-analysis of randomized controlled trials

2013· review· en· W2122498223 on OpenAlexaboutno aff
Hua Liu, Chao Zeng, Shuguang Gao, Tuo Yang, Wei Luo, Yusheng Li, Yilin Xiong, Jin‐Peng Sun, Guanghua Lei

Bibliographic record

VenueJournal of International Medical Research · 2013
Typereview
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisRandomized controlled trialMeta-analysisPhysical therapyStrictly standardized mean differencePain reliefVisual analogue scaleMEDLINESurgeryInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: A meta-analysis was conducted to examine the effect of mud therapy on pain relief in patients with knee osteoarthritis (OA). METHODS: A detailed search of PubMed®/MEDLINE® was undertaken to identify randomized controlled trials and prospective comparative studies published before 9 March 2013 that compared mud therapy with control group treatments in patients with knee OA. RESULTS: A quantitative meta-analysis of seven studies (410 patients) was performed. There was a significant difference between the groups in the visual analogue scale pain score (standardized mean difference [SMD] -0.73) and Western Ontario and McMaster Universities Osteoarthritis Index pain score (SMD -0.30), with differences in favour of mud therapy. CONCLUSIONS: Mud therapy is a favourable option for pain relief in patients with knee OA. Additional high-quality randomized controlled trials need to be conducted to explore this issue further and to confirm this conclusion.

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)0.014
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.028
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0180.032
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
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 head0.241
GPT teacher head0.573
Teacher spread0.332 · 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 designMeta-analysis
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

Citations45
Published2013
Admission routes1
Has abstractyes

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