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Record W2187417018 · doi:10.1016/s0254-6272(15)30118-7

Effect of Huqian Wan on liver-Yin and kidney-Yin deficiency patterns in patients with knee osteoarthritis

2015· article· en· W2187417018 on OpenAlexaboutno aff
Qiqing Chen, Hongting Jin, Bin He, Liang Wang

Bibliographic record

VenueJournal of Traditional Chinese Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWOMACOsteoarthritisVisual analogue scaleInternal medicineQuality of life (healthcare)Traditional Chinese medicinePhysical therapySurgeryAlternative medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To observe the curative effect of Huqian Wan on liver and kidney-Yin deficiency knee osteoarthritis (KOA). METHODS: One hundred patients were randomly divided, into a treatment (50 patients) and control group (50 patients). In the treatment group, patients orally took the Chinese medicine Huqian Wan. Control group patients orally took Votalin, 75 mg, once a day, for 8 weeks. The visual analog scale (VAS), Western Ontario and McMaster University Osteoarthritis Index (WOMAC), and Medical Outcomes Study Short Form 36-Item Health Survey (SF 36) were used to evaluate the curative effect before treatment and after 8 and 16 weeks of treatment. RESULTS: VAS and WOMAC scores significantly decreased and SF 36 scores significantly increased after treatment in both groups compared with before treatment (P < 0.05). There were significant differences in VAS, WOMAC, and SF 36 score changes between the two groups at week 16 (P < 0.05). There was a significant increase in VAS and WOMAC scores in the control groups (P < 0.05) between weeks 8 and 16, but no significant difference was found in the treatment group (P > 0.05). CONCLUSION: Huqian Wan could effectively improve the clinical symptoms and quality of life in patients with KOA. It could also have a better and longer lasting curative effect without obvious adverse events compared with Votalin.

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)0.001
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 score0.246
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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